[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-IoT":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"IoT","tag",[4],75,1,100,[11,132,241,314,372,434,489,543,608,671,737,810,880,952,1000,1072,1129,1193,1256,1312,1383,1452,1511,1568,1638,1698,1759,1812,1862,1925,2002,2057,2116,2170,2232,2281,2330,2393,2446,2512,2569,2611,2683,2744,2799,2871,2926,2986,3041,3094,3146,3210,3260,3324,3407,3452,3497,3558,3619,3673,3736,3775,3830,3892,3960,4015,4063,4108,4180,4222,4306,4376,4438,4482,4526],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":14,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":7,"score_detail":23,"sources":29,"tags":31,"primary_topic":37,"secondary_topics":42,"classification":43,"search_phrases":115,"slug":118,"view_count":119,"doi":120,"paper":121,"created_at":131},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。",null,"International Journal of Drug Delivery Technology","2026-10-21T00:00:00Z","论文",10,false,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":21,"relevant":8,"comment":28},18,22,17,8,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[30],{"name":18,"url":14},[32,33,34,35,36],"智慧农业","农业人工智能","物联网","杂草识别","病虫害监测",{"code":38,"slug":39,"name":40,"path":41,"parent_code":17},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":44,"locked":22,"version":45,"method":46,"evidence":47},"partial","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51,"reason":114},[49,61,68,77,85,93,100,107],{"code":50,"score":24,"supported":51,"title":52,"abstract":54,"tags":60},"I2",true,[53],"deep learning",[55,56,57,53,58,59],"深度学习","计算机视觉","病虫害识别","artificial intelligence","computer vision",[33],{"code":62,"score":24,"supported":51,"title":63,"abstract":64,"tags":67},"I5",[4],[34,65,4,66],"internet of things","edge computing",[34],{"code":69,"score":21,"supported":51,"title":70,"abstract":71,"tags":76},"F4",[],[72,73,74,75],"农药","除草","pesticide","crop protection",[35],{"code":78,"score":79,"supported":51,"title":80,"abstract":81,"tags":84},"I1",7,[],[82,83],"精准农业","precision agriculture",[32],{"code":86,"score":87,"supported":51,"title":88,"abstract":89,"tags":92},"A2",6,[],[90,91],"小麦","wheat",[],{"code":94,"score":87,"supported":51,"title":95,"abstract":96,"tags":99},"A3",[],[97,98],"玉米","maize",[],{"code":101,"score":87,"supported":51,"title":102,"abstract":103,"tags":106},"A4",[],[104,105],"大豆","soybean",[],{"code":108,"score":87,"supported":51,"title":109,"abstract":110,"tags":113},"B1",[],[111,112],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[116,117],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556",0,"10.25258\u002Fijddt.16.76s.32",{"doi":120,"openalex_id":122,"authors":123,"venue":18,"cited_by_count":119,"oa_url":14,"card":124,"direction":128,"ingested_from":130},"W7223145687",[],{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","智慧农业 \u002F 农业物联网","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","openalex","2026-10-22T11:20:18.540359Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":17,"source_name":138,"source_url":135,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":139,"score_detail":140,"sources":144,"tags":146,"primary_topic":148,"secondary_topics":152,"classification":163,"search_phrases":216,"slug":219,"view_count":119,"doi":220,"paper":221,"created_at":240},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":24,"substance":25,"depth":24,"authority":141,"freshness":142,"relevant":8,"comment":143},13,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[145],{"name":138,"url":135},[32,33,34,82,147],"气候韧性",{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[153,157],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":164,"locked":22,"version":45,"method":46,"evidence":165},"classified",{"candidates":166,"agricultural_evidence":51},[167,173,178,183,189,196,203,210],{"code":50,"score":24,"supported":51,"title":168,"abstract":169,"tags":172},[58],[55,170,56,171,53,59],"机器学习","machine learning",[33],{"code":78,"score":174,"supported":51,"title":175,"abstract":176,"tags":177},16,[83],[82,83],[32,82],{"code":62,"score":21,"supported":51,"title":179,"abstract":180,"tags":182},[],[34,181,4,66],"边缘计算",[34],{"code":158,"score":142,"supported":51,"title":184,"abstract":185,"tags":188},[],[186,187,159],"灌溉","水资源",[],{"code":190,"score":79,"supported":51,"title":191,"abstract":192,"tags":195},"H7",[],[193,194],"气候变化","climate change",[147],{"code":197,"score":87,"supported":51,"title":198,"abstract":199,"tags":202},"H2",[],[200,201],"土壤","soil",[],{"code":204,"score":205,"supported":51,"title":206,"abstract":207,"tags":209},"A7",3,[],[208],"间作",[],{"code":211,"score":205,"supported":51,"title":212,"abstract":213,"tags":215},"F3",[],[214],"养分管理",[],[217,218],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":220,"openalex_id":222,"authors":223,"venue":138,"cited_by_count":119,"oa_url":135,"card":234,"direction":128,"ingested_from":130},"W7223121456",[224,227,229,231],{"name":225,"orcid":226},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":228,"orcid":17},"Oluwadare OYEBODE",{"name":230,"orcid":17},"Iretiayo Adelaiye",{"name":232,"orcid":233},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":17,"source_name":247,"source_url":244,"published_at":248,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":250,"sources":254,"tags":256,"primary_topic":260,"secondary_topics":263,"classification":264,"search_phrases":291,"slug":294,"view_count":119,"doi":295,"paper":296,"created_at":313},5389,"A Wideband Flexible Waterproof Antenna for Internet of Underground Things: Design, Characterization, and Field Validation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204815","The Internet of Underground Things (IoUT) is seen as a new technology that could revolutionize soil management, improving irrigation efficiency, soil and plant health, and food safety using reliable and resilient wireless connectivity. Rapid deployment of IoUT in precision agriculture requires flexible, waterproof antennas specifically designed for operation in soil. This study introduces a methodology for the design and characterization of waterproof, flexible antennas that maintain robust performance in soil across varying gravimetric water contents (GWC). Applying this methodology, a wideband, waterproof, and flexible antenna was designed and fabricated. Its parameters and characteristics were evaluated numerically and experimentally in soils with different GWCs to determine its suitability for IoUT applications. Within the GWC range of 10% to 20%, which is optimal for supporting plant health and water use, the antenna demonstrates a wide bandwidth of 0.6 to 1.8 GHz (|S11| \u003C −10 dB) and radiation efficiency ranging from 3.56% to 0.15% at 868 MHz. Experimental results obtained in free space, dry sand, and garden soil agree reasonably well with simulation predictions, showing that the proposed antenna is suitable for IoUT applications. Field validation demonstrated that the antenna prototype maintained its impedance response after two months of in-soil operation, indicating stable performance over the evaluated period.","地下物联网（Internet of Underground Things, IoUT）被视为一项可能彻底改变土壤管理的新技术，通过可靠且具有韧性的无线连接提升灌溉效率、土壤与植物健康以及食品安全。在精准农业中快速部署IoUT需要专为土壤环境运行而设计的柔性防水天线。本研究提出了一种防水柔性天线的设计与表征方法，使天线在不同重量含水率（gravimetric water content, GWC）的土壤中保持稳健性能。应用该方法，设计并制作了一款宽带防水柔性天线。在不同GWC的土壤中对其参数与特性进行了数值仿真与实验评估，以确定其用于IoUT应用的适用性。在10%至20%的GWC范围内（该范围最有利于维持植物健康与水分利用），天线在868 MHz处展现出0.6至1.8 GHz的宽带宽（|S11| \u003C −10 dB）以及3.56%至0.15%的辐射效率。在自由空间、干沙和花园土壤中获得的实验结果与仿真预测吻合良好，表明所提出的天线适用于IoUT应用。现场验证表明，天线原型在土壤中运行两个月后仍保持其阻抗响应，表明在所评估时段内性能稳定。","Electronics","2026-10-19T00:00:00Z",70,{"impact":251,"substance":252,"depth":26,"authority":141,"freshness":27,"relevant":8,"comment":253},12,20,"面向地下物联网的柔性防水天线设计与田间验证，方法新颖、数据扎实，对精准农业无线感知有参考价值，但属细分技术进展，公共影响有限。",[255],{"name":247,"url":244},[32,34,257,258,259],"土壤水分","地下物联网","柔性天线",{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":164,"locked":22,"version":45,"method":46,"evidence":265},{"candidates":266,"agricultural_evidence":51},[267,271,275,282,286],{"code":197,"score":79,"supported":51,"title":268,"abstract":269,"tags":270},[],[200,201],[257],{"code":78,"score":79,"supported":51,"title":272,"abstract":273,"tags":274},[],[82,83],[32],{"code":276,"score":87,"supported":51,"title":277,"abstract":278,"tags":281},"E4",[],[279,280],"食品安全","food safety",[],{"code":158,"score":87,"supported":51,"title":283,"abstract":284,"tags":285},[],[186,159],[],{"code":62,"score":287,"supported":51,"title":288,"abstract":289,"tags":290},4,[],[34],[34],[292,293],"IoUT 柔性防水天线","地下物联网 土壤水分","IoUT柔性防水天线-5389","10.3390\u002Felectronics15204815",{"doi":295,"openalex_id":297,"authors":298,"venue":247,"cited_by_count":119,"oa_url":244,"card":308,"direction":128,"ingested_from":130},"W7222978032",[299,302,305],{"name":300,"orcid":301},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":303,"orcid":304},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":306,"orcid":307},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":309,"method":310,"finding":311,"direction":128,"opportunity":312},"设计并验证了一款用于地下物联网的宽带柔性防水天线。","采用数值仿真与实验测试，在不同含水率土壤中评估天线性能。","在10%-20%含水率下带宽0.6-1.8GHz，埋土两个月阻抗响应稳定。","可探索天线辐射效率随土壤含水率下降的补偿机制，或结合土壤传感节点做长期田间组网验证。","2026-10-20T23:30:13.823825Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":17,"source_name":320,"source_url":317,"published_at":321,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":119,"score_detail":322,"sources":324,"tags":326,"primary_topic":17,"secondary_topics":328,"classification":329,"search_phrases":348,"slug":351,"view_count":119,"doi":352,"paper":353,"created_at":371},5386,"From Ancient Records to Agentic Intelligence: A Comprehensive Historical Review of Data Science and Its Role in Intelligent Internet of Things Analytics","https:\u002F\u002Fdoi.org\u002F10.58496\u002Fbjiot\u002F2026\u002F014","Since the era of the Mesopotamia between 2900 BCE - 2350 BCE, the goals of “data science-like” approaches have always revolved around collecting fragments of information and trying to make insights out of such information. Data Science was not clearly termed as Data Science from the onset. Moreover, there exists several questions about Data Science just been a polished name for Statistics. Therefore, there is a need to look carefully at how Data Science has evolved over the years and where it is currently. The study defines Data Science as a multidisciplinary field that integrates statistics, mathematics, computer science, domain expertise, and ethics. This is important because, it will give Data Scientists and the Data Science enthusiasts a clear understanding of this discipline. In addition, the paper demonstrates how Data Science transforms massive Internet of Things (IoT) data streams into actionable insights through predictive analytics, anomaly detection, explainable artificial intelligence, cybersecurity analytics, digital twins, and intelligent decision support. The findings reveal that the evolution of Data Science reflects a transition from descriptive statistical analysis to autonomous, explainable, and trustworthy intelligent systems capable of supporting real-time decision-making across diverse domains. This work provides a unified historical perspective, highlights emerging research directions, and offers a valuable reference for researchers, educators, and practitioners seeking to understand the past, present, and future direction of Data Science.","自公元前2900年至公元前2350年的美索不达米亚时代以来，“类数据科学”方法的目标始终围绕收集信息碎片并试图从中提炼洞见。数据科学从一开始并未被明确命名为数据科学。此外，关于数据科学是否只是统计学的一个精致名称，仍存在若干疑问。因此，有必要仔细审视数据科学多年来的演变历程及其当前所处的位置。本研究将数据科学定义为一个融合统计学、数学、计算机科学、领域专业知识与伦理学的多学科领域。这一定义具有重要意义，因为它能使数据科学家和数据科学爱好者对该学科形成清晰的认识。此外，本文展示了数据科学如何通过预测分析、异常检测、可解释人工智能、网络安全分析、数字孪生和智能决策支持，将海量物联网（IoT）数据流转化为可付诸行动的洞见。研究结果表明，数据科学的演变反映了从描述性统计分析向自主、可解释且可信的智能系统的转变，这些系统能够支持跨领域的实时决策。本研究提供了统一的历史视角，突出了新兴研究方向，并为试图理解数据科学过去、现在与未来方向的研究者、教育者和从业者提供了有价值的参考。","Babylonian Journal of Internet of Things","2026-10-18T00:00:00Z",{"impact":119,"substance":119,"depth":119,"authority":119,"freshness":119,"relevant":119,"comment":323},"该文为数据科学通史综述，仅泛泛提及物联网分析，未涉及农业场景，与三农及农业信息化无实质关联。",[325],{"name":320,"url":317},[33,34,327],"数据科学",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":331},"needs_review",{"candidates":332,"agricultural_evidence":22,"reason":347},[333,337,342],{"code":62,"score":24,"supported":51,"title":334,"abstract":335,"tags":336},[65],[34,65,4],[34],{"code":50,"score":79,"supported":51,"title":338,"abstract":339,"tags":341},[],[58,340],"decision support",[33],{"code":78,"score":205,"supported":51,"title":343,"abstract":344,"tags":346},[],[345],"数字孪生",[],"农业相关性需要复核",[349,350],"农业人工智能 数据科学 物联网","农业人工智能 数据科学","农业人工智能数据科学物联网-5386","10.58496\u002Fbjiot\u002F2026\u002F014",{"doi":352,"openalex_id":354,"authors":355,"venue":320,"cited_by_count":119,"oa_url":365,"card":366,"direction":128,"ingested_from":130},"W7222906119",[356,359,362],{"name":357,"orcid":358},"Aamo Iorliam","https:\u002F\u002Forcid.org\u002F0000-0001-8238-9686",{"name":360,"orcid":361},"Iveren Blessing Iorliam","https:\u002F\u002Forcid.org\u002F0000-0002-9973-6151",{"name":363,"orcid":364},"Yahaya Isah Shehu","https:\u002F\u002Forcid.org\u002F0000-0001-8924-9344","https:\u002F\u002Fmesopotamian.press\u002Fjournals\u002Findex.php\u002FBJIoT\u002Farticle\u002Fdownload\u002F1197\u002F1231",{"tldr":367,"method":368,"finding":369,"direction":238,"opportunity":370},"综述数据科学从古代记录到智能体智能的历史演进及其在物联网分析中的作用。","历史文献综述，梳理数据科学跨学科定义与物联网分析技术。","数据科学已从描述性统计转向自主、可解释、可信的实时智能决策系统。","可将智能体智能与可解释AI引入农业物联网实时决策，探索可信自主农事决策系统。","2026-10-20T23:30:10.332642Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":17,"source_name":247,"source_url":375,"published_at":248,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":386,"secondary_topics":387,"classification":389,"search_phrases":410,"slug":413,"view_count":119,"doi":414,"paper":415,"created_at":433},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。",67,{"impact":251,"substance":24,"depth":174,"authority":141,"freshness":27,"relevant":8,"comment":380},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[382],{"name":247,"url":375},[32,34,82,384,385],"节水灌溉","LoRa",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[388],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":390},{"candidates":391,"agricultural_evidence":51},[392,396,401,405],{"code":62,"score":24,"supported":51,"title":393,"abstract":394,"tags":395},[4],[34,65,4],[34],{"code":158,"score":397,"supported":51,"title":398,"abstract":399,"tags":400},15,[159],[186,159],[384],{"code":197,"score":87,"supported":51,"title":402,"abstract":403,"tags":404},[],[200,201],[],{"code":78,"score":406,"supported":22,"title":407,"abstract":408,"tags":409},2,[],[],[32,82],[411,412],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":414,"openalex_id":416,"authors":417,"venue":247,"cited_by_count":119,"oa_url":375,"card":428,"direction":128,"ingested_from":130},"W7222959710",[418,420,422,425],{"name":419,"orcid":17},"Hanfoug Salah",{"name":421,"orcid":17},"Riad Saidi",{"name":423,"orcid":424},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":426,"orcid":427},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":429,"method":430,"finding":431,"direction":128,"opportunity":432},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":17,"source_name":440,"source_url":437,"published_at":321,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":441,"sources":443,"tags":445,"primary_topic":449,"secondary_topics":450,"classification":451,"search_phrases":469,"slug":472,"view_count":119,"doi":473,"paper":474,"created_at":488},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology",{"impact":251,"substance":252,"depth":174,"authority":141,"freshness":142,"relevant":8,"comment":442},"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[444],{"name":440,"url":437},[32,34,446,447,448],"水培","增强现实","数据过载",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":452},{"candidates":453,"agricultural_evidence":51},[454,458,463],{"code":62,"score":21,"supported":51,"title":455,"abstract":456,"tags":457},[],[34,65,4],[34],{"code":78,"score":79,"supported":51,"title":459,"abstract":460,"tags":462},[],[32,461],"smart agriculture",[32],{"code":464,"score":287,"supported":51,"title":465,"abstract":466,"tags":468},"B7",[],[467],"hydroponic",[446],[470,471],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":473,"openalex_id":475,"authors":476,"venue":440,"cited_by_count":119,"oa_url":437,"card":483,"direction":128,"ingested_from":130},"W7222865525",[477,480],{"name":478,"orcid":479},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":481,"orcid":482},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":484,"method":485,"finding":486,"direction":128,"opportunity":487},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z",{"id":490,"title":491,"url":492,"summary":493,"summary_zh":17,"content":17,"source_name":494,"source_url":492,"published_at":495,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":496,"score_detail":497,"sources":500,"tags":502,"primary_topic":17,"secondary_topics":506,"classification":507,"search_phrases":522,"slug":525,"view_count":119,"doi":526,"paper":527,"created_at":542},5239,"Development and Performance Evaluation of an IoT-Based Smart Irrigation System with Real-Time Data Monitoring for Beetroot (Beta vulgaris L.)","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11385500\u002Fv1","Development and Performance Evaluation of an IoT-Based Smart Irrigation System with Real-Time Data Monitoring for Beetroot (Beta vulgaris L.)。Research Square","Research Square","2026-10-16T00:00:00Z",53,{"impact":27,"substance":174,"depth":498,"authority":87,"freshness":142,"relevant":8,"comment":499},14,"预印本论文，聚焦物联网智能灌溉与实时监测，方法有参考价值但尚未经同行评审，影响范围限于细分领域。",[501],{"name":494,"url":492},[32,34,503,504,505],"智能灌溉","实时监测","甜菜根",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":508},{"candidates":509,"agricultural_evidence":22,"reason":347},[510,514,518],{"code":158,"score":251,"supported":51,"title":511,"abstract":512,"tags":513},[159],[159],[503],{"code":62,"score":251,"supported":51,"title":515,"abstract":516,"tags":517},[4],[4],[34],{"code":78,"score":8,"supported":22,"title":519,"abstract":520,"tags":521},[],[],[32],[523,524],"IoT 智能灌溉 甜菜根","Research Square 智能灌溉","IoT智能灌溉甜菜根-5239","10.21203\u002Frs.3.rs-11385500\u002Fv1",{"doi":526,"openalex_id":528,"authors":529,"venue":494,"cited_by_count":119,"oa_url":541,"card":17,"direction":128,"ingested_from":130},"W7222511746",[530,532,534,537,539],{"name":531,"orcid":17},"Soulaiman Mwamini",{"name":533,"orcid":17},"Triphonie Uwamariya",{"name":535,"orcid":536},"Jean Nepo Nsengiyumva","https:\u002F\u002Forcid.org\u002F0000-0002-0607-1825",{"name":538,"orcid":17},"Venant Kubwimana",{"name":540,"orcid":17},"Marie Jeanne Umuhoza","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11385500\u002Flatest.pdf","2026-10-18T23:30:09.469477Z",{"id":544,"title":545,"url":546,"summary":547,"summary_zh":548,"content":17,"source_name":549,"source_url":546,"published_at":550,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":551,"score_detail":552,"sources":554,"tags":556,"primary_topic":559,"secondary_topics":564,"classification":566,"search_phrases":583,"slug":586,"view_count":119,"doi":587,"paper":588,"created_at":607},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":27,"substance":24,"depth":174,"authority":87,"freshness":27,"relevant":8,"comment":553},"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[555],{"name":549,"url":546},[32,34,82,557,558],"温室大棚","水培蔬菜",{"code":464,"slug":560,"name":561,"path":562,"parent_code":563},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[565],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":567},{"candidates":568,"agricultural_evidence":51},[569,573,579],{"code":62,"score":24,"supported":51,"title":570,"abstract":571,"tags":572},[4],[34,65,4],[34],{"code":464,"score":498,"supported":51,"title":574,"abstract":576,"tags":578},[575],"greenhouse",[577,575],"温室",[],{"code":78,"score":27,"supported":51,"title":580,"abstract":581,"tags":582},[],[82,83],[32,82],[584,585],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":587,"openalex_id":589,"authors":590,"venue":549,"cited_by_count":119,"oa_url":601,"card":602,"direction":128,"ingested_from":130},"W7222850582",[591,593,596,599],{"name":592,"orcid":17},"Ramadanzah Ramadanzah",{"name":594,"orcid":595},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":597,"orcid":598},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":600,"orcid":17},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":603,"method":604,"finding":605,"direction":128,"opportunity":606},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":609,"title":610,"url":611,"summary":612,"summary_zh":613,"content":17,"source_name":614,"source_url":611,"published_at":495,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":7,"score_detail":615,"sources":618,"tags":620,"primary_topic":623,"secondary_topics":624,"classification":625,"search_phrases":655,"slug":658,"view_count":119,"doi":659,"paper":660,"created_at":670},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering",{"impact":174,"substance":616,"depth":26,"authority":251,"freshness":142,"relevant":8,"comment":617},21,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[619],{"name":614,"url":611},[32,33,34,621,622],"作物胁迫","遥感监测",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":626},{"candidates":627,"agricultural_evidence":51},[628,632,639,643,647,651],{"code":62,"score":24,"supported":51,"title":629,"abstract":630,"tags":631},[4],[34,65,4],[34],{"code":633,"score":79,"supported":51,"title":634,"abstract":635,"tags":638},"I3",[],[636,637],"遥感","remote sensing",[622],{"code":197,"score":87,"supported":51,"title":640,"abstract":641,"tags":642},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":644,"abstract":645,"tags":646},[],[186,159],[],{"code":78,"score":8,"supported":22,"title":648,"abstract":649,"tags":650},[],[],[32],{"code":50,"score":8,"supported":22,"title":652,"abstract":653,"tags":654},[],[],[33],[656,657],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":659,"openalex_id":661,"authors":662,"venue":614,"cited_by_count":119,"oa_url":611,"card":665,"direction":128,"ingested_from":130},"W7222730658",[663],{"name":664,"orcid":17},"Sunitha Thankappan",{"tldr":666,"method":667,"finding":668,"direction":128,"opportunity":669},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":672,"title":673,"url":674,"summary":675,"summary_zh":676,"content":17,"source_name":677,"source_url":674,"published_at":678,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":679,"score_detail":680,"sources":682,"tags":684,"primary_topic":686,"secondary_topics":687,"classification":688,"search_phrases":718,"slug":721,"view_count":8,"doi":722,"paper":723,"created_at":736},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":27,"substance":498,"depth":141,"authority":87,"freshness":27,"relevant":8,"comment":681},"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[683],{"name":677,"url":674},[32,33,34,636,685],"农业教育",{"code":38,"slug":39,"name":40,"path":41,"parent_code":17},[],{"status":44,"locked":22,"version":45,"method":46,"evidence":689},{"candidates":690,"agricultural_evidence":51,"reason":114},[691,695,699,703,707,714],{"code":78,"score":397,"supported":51,"title":692,"abstract":693,"tags":694},[461],[32,461],[32],{"code":633,"score":79,"supported":51,"title":696,"abstract":697,"tags":698},[],[636,637],[636],{"code":62,"score":79,"supported":51,"title":700,"abstract":701,"tags":702},[],[34,65],[34],{"code":158,"score":87,"supported":51,"title":704,"abstract":705,"tags":706},[],[186,159],[],{"code":708,"score":87,"supported":51,"title":709,"abstract":710,"tags":713},"I4",[],[711,712],"地理信息","GIS",[],{"code":50,"score":287,"supported":51,"title":715,"abstract":716,"tags":717},[],[58],[33],[719,720],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":722,"openalex_id":724,"authors":725,"venue":677,"cited_by_count":119,"oa_url":729,"card":730,"direction":128,"ingested_from":130},"W7221997102",[726],{"name":727,"orcid":728},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":731,"method":732,"finding":733,"direction":734,"opportunity":735},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","数字乡村与农业信息化","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","2026-10-16T23:30:07.890026Z",{"id":738,"title":739,"url":740,"summary":741,"summary_zh":742,"content":17,"source_name":743,"source_url":740,"published_at":744,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":745,"sources":747,"tags":749,"primary_topic":752,"secondary_topics":753,"classification":754,"search_phrases":788,"slug":791,"view_count":119,"doi":17,"paper":792,"created_at":809},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",{"impact":251,"substance":252,"depth":26,"authority":141,"freshness":27,"relevant":8,"comment":746},"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[748],{"name":743,"url":740},[32,170,34,750,751],"水产养殖","水质监测",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":755},{"candidates":756,"agricultural_evidence":51},[757,761,767,771,777,783],{"code":62,"score":21,"supported":51,"title":758,"abstract":759,"tags":760},[],[34,65,4],[34],{"code":78,"score":79,"supported":51,"title":762,"abstract":763,"tags":766},[],[764,765],"数字农业","digital agriculture",[32],{"code":50,"score":79,"supported":51,"title":768,"abstract":769,"tags":770},[],[170,171],[170],{"code":772,"score":287,"supported":51,"title":773,"abstract":774,"tags":776},"D",[],[775],"水产",[750],{"code":778,"score":205,"supported":51,"title":779,"abstract":780,"tags":782},"C",[],[781],"livestock",[],{"code":784,"score":8,"supported":22,"title":785,"abstract":786,"tags":787},"D4",[],[],[751],[789,790],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":17,"openalex_id":793,"authors":794,"venue":743,"cited_by_count":119,"oa_url":740,"card":804,"direction":128,"ingested_from":130},"W7221732805",[795,797,799,801],{"name":796,"orcid":17},"Wanarase Sinhashthita",{"name":798,"orcid":17},"Tossaporn Alherbe",{"name":800,"orcid":17},"Kamol Chareonsri",{"name":802,"orcid":803},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":805,"method":806,"finding":807,"direction":128,"opportunity":808},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z",{"id":811,"title":812,"url":813,"summary":814,"summary_zh":815,"content":17,"source_name":816,"source_url":813,"published_at":817,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":818,"score_detail":819,"sources":821,"tags":823,"primary_topic":825,"secondary_topics":826,"classification":827,"search_phrases":856,"slug":859,"view_count":205,"doi":860,"paper":861,"created_at":879},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":27,"substance":498,"depth":174,"authority":87,"freshness":27,"relevant":8,"comment":820},"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[822],{"name":816,"url":813},[32,33,824,34,82],"农业机器人",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":828},{"candidates":829,"agricultural_evidence":51},[830,834,839,843,847,851],{"code":62,"score":24,"supported":51,"title":831,"abstract":832,"tags":833},[4],[34,65,4],[34],{"code":78,"score":835,"supported":51,"title":836,"abstract":837,"tags":838},11,[],[32,82,83],[32,82],{"code":50,"score":79,"supported":51,"title":840,"abstract":841,"tags":842},[],[58,340],[33],{"code":197,"score":87,"supported":51,"title":844,"abstract":845,"tags":846},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":848,"abstract":849,"tags":850},[],[186,159],[],{"code":852,"score":8,"supported":22,"title":853,"abstract":854,"tags":855},"F6",[],[],[824],[857,858],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877","10.22214\u002Fijraset.2026.85108",{"doi":860,"openalex_id":862,"authors":863,"venue":816,"cited_by_count":119,"oa_url":813,"card":874,"direction":128,"ingested_from":130},"W7221682750",[864,866,868,870,872],{"name":865,"orcid":17},"Chaitra A S",{"name":867,"orcid":17},"Venugopal Gowda V",{"name":869,"orcid":17},"Vinay H",{"name":871,"orcid":17},"Jeevan T",{"name":873,"orcid":17},"Puneeth M",{"tldr":875,"method":876,"finding":877,"direction":128,"opportunity":878},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z",{"id":881,"title":882,"url":883,"summary":884,"summary_zh":885,"content":17,"source_name":886,"source_url":883,"published_at":887,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":888,"score_detail":889,"sources":891,"tags":893,"primary_topic":896,"secondary_topics":901,"classification":902,"search_phrases":931,"slug":934,"view_count":406,"doi":17,"paper":935,"created_at":951},4669,"REVIEW ON RENEWABLE ENERGY-BASED SMART FERTIGATION CONTROLLER USING NPK SENSOR","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F302","Agriculture will require efficient management of water, fertilizers, and energy to improve crop productivity and support sustainable farming practices. Traditional fertilizer application methods will often depend on manual observation and fixed schedules, which will lead to improper nutrient management, nutrient imbalance, and unnecessary fertilizer usage. Smart fertigation systems will provide an effective approach for addressing these limitations by integrating IoT, soil nutrient sensing, and real-time monitoring technologies. In this review, NPK-based soil monitoring and IoT-enabled fertigation approaches will be studied with emphasis on nitrogen, phosphorus, and potassium monitoring for precision agriculture. The proposed system will use an ESP32 microcontroller, NPK sensor, soil moisture sensor, and DHT11 sensor to monitor soil nutrient and environmental conditions in real time. The collected data will be displayed on an LCD and transmitted through Wi-Fi to the Blynk IoT platform for remote monitoring. Based on real-time NPK readings, the fertilizer requirement will be determined, and the appropriate fertilizer-supply motor will be manually operated. Solar energy will be integrated with a solar charge controller and rechargeable battery to reduce dependence on conventional electricity and support sustainable operation. The reviewed approaches will indicate that the integration of NPK sensors, IoT monitoring, fertigation, and renewable energy will provide a promising solution for improving fertilizer utilization, reducing resource wastage, minimizing manual effort, and supporting sustainable precision agriculture.","农业需要高效管理水、肥料和能源，以提高作物生产力并支持可持续农业实践。传统施肥方法往往依赖人工观察和固定日程，这会导致养分管理不当、养分失衡和不必要的肥料使用。智能水肥一体化系统通过整合物联网（IoT）、土壤养分传感和实时监测技术，为解决这些局限提供了一种有效途径。本文综述了基于氮磷钾（NPK）的土壤监测和物联网水肥一体化方法，重点关注精准农业中的氮、磷、钾监测。所提出的系统将使用ESP32微控制器、NPK传感器、土壤湿度传感器和DHT11传感器实时监测土壤养分和环境条件。采集的数据将显示在LCD上，并通过Wi-Fi传输至Blynk物联网平台进行远程监测。根据实时NPK读数确定肥料需求量，并手动操作相应的供肥电机。将太阳能与太阳能充电控制器和可充电电池集成，以减少对传统电力的依赖并支持可持续运行。综述的方法表明，将NPK传感器、物联网监测、水肥一体化和可再生能源相结合，为提高肥料利用率、减少资源浪费、降低人工投入和支持可持续精准农业提供了一种有前景的解决方案。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",46,{"impact":27,"substance":498,"depth":251,"authority":87,"freshness":87,"relevant":8,"comment":890},"综述性论文，提出基于ESP32与NPK传感器的太阳能智能水肥一体化方案，有一定技术参考价值，但属常规技术集成，非重大突破，且期刊权威性一般。",[892],{"name":886,"url":883},[32,34,82,894,895],"水肥一体化","可再生能源",{"code":211,"slug":897,"name":898,"path":899,"parent_code":900},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":164,"locked":22,"version":45,"method":46,"evidence":903},{"candidates":904,"agricultural_evidence":51},[905,913,917,921,925],{"code":211,"score":24,"supported":51,"title":906,"abstract":908,"tags":912},[907],"fertigation",[909,910,894,214,911,907],"施肥","肥料","fertilizer",[894],{"code":78,"score":27,"supported":51,"title":914,"abstract":915,"tags":916},[],[82,83],[32,82],{"code":62,"score":79,"supported":51,"title":918,"abstract":919,"tags":920},[],[34,4],[34],{"code":197,"score":87,"supported":51,"title":922,"abstract":923,"tags":924},[],[200,201],[],{"code":926,"score":205,"supported":51,"title":927,"abstract":928,"tags":930},"H8",[],[929],"可持续农业",[],[932,933],"ESP32 NPK传感器 智能灌溉","太阳能 水肥一体化 控制器","ESP32NPK传感器智能灌溉-4669",{"doi":17,"openalex_id":936,"authors":937,"venue":886,"cited_by_count":119,"oa_url":883,"card":946,"direction":128,"ingested_from":130},"W7221368129",[938,940,942,944],{"name":939,"orcid":17},"Niharika Bodake",{"name":941,"orcid":17},"Akshata Gaurkar",{"name":943,"orcid":17},"Vaishnavi Chavan",{"name":945,"orcid":17},"S. K. Godase",{"tldr":947,"method":948,"finding":949,"direction":128,"opportunity":950},"综述基于NPK传感器与物联网的可再生能源智能水肥一体化控制器方案。","ESP32、NPK\u002F土壤湿度\u002FDHT11传感器、Blynk平台与太阳能供电。","NPK传感+物联网+水肥一体化+可再生能源可提升肥料利用率并减少浪费。","肥料供给仍靠手动，可研究基于实时NPK的自动决策与闭环变量施肥控制。","2026-10-11T23:30:16.034599Z",{"id":953,"title":954,"url":955,"summary":956,"summary_zh":957,"content":17,"source_name":958,"source_url":955,"published_at":887,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":119,"score_detail":959,"sources":961,"tags":963,"primary_topic":17,"secondary_topics":967,"classification":968,"search_phrases":983,"slug":986,"view_count":8,"doi":17,"paper":987,"created_at":999},4666,"AUTONOMOUS BOREWELL RESCUE ROBOT WITH LIVE VIDEO SURVEILLANCE","https:\u002F\u002Fijdim.com\u002Fjournal\u002Findex.php\u002Fijdim\u002Farticle\u002Fview\u002F1442","Children falling into open or abandoned borewells is a recurring tragedy in many parts of India. Borewells are drilled to depths of several hundred feet with a diameter that is often less than a foot, and when they are left uncovered a small child can slip inside and become trapped at a depth where no adult can reach. Conventional rescue involves digging a parallel pit with heavy machinery, which can take more than a day and exposes the child to lack of oxygen, low temperature, falling soil, and dehydration. This paper presents an autonomous borewell rescue robot with live video surveillance that is designed to reduce this delay. The proposed robot is a compact cylindrical unit that is lowered into the borewell on a steel cable driven by a motorised winch at the surface. It carries a camera with infrared illumination, a temperature and humidity sensor, a gas sensor for oxygen depletion and harmful gases, an ultrasonic sensor for distance measurement, and a microphone for listening to the child. A Raspberry Pi on the robot handles video capture and streaming, while an Arduino or ESP32 controls the gripper motors, legtype wall supports, and sensor sampling. Live video and sensor data are sent over a shielded cable or Wi-Fi link to a control station at the surface. The robot descends autonomously under controlled speed, using the ultrasonic sensor and camera to stop at a safe distance above the child. Its wall-gripping legs expand against the borewell wall to stabilise the body, after which a set of padded arms is positioned around the child under the supervision of the operator. An oxygen supply tube can be guided down along the cable, and a two-way audio channel allows parents or rescuers to talk to the child and keep the child calm throughout the operation. All sensor readings, including depth, temperature, gas levels, and the status of each actuator, are displayed on a laptop or tablet dashboard at the surface and are also pushed to a cloud server through a mobile network. District officials and remote experts can view the live stream and readings through a web page, which supports coordination between the fire department, disaster response teams, and medical staff. Automatic alerts are generated when oxygen falls below safe levels or when the gripper force exceeds a set limit. The system is intended to support trained rescuers and not to act without human supervision during the critical lifting phase. Tests on a vertical PVC pipe model with a weighted dummy showed that the robot could reach a depth of several metres, hold its position steadily, and lift the dummy without slipping while the operator watched a continuous video feed. Future work includes a narrower mechanical design for smaller borewells, depth rating for deeper shafts, and computer vision to assist in identifying the posture of the trapped child.","儿童坠入敞口或废弃的钻井（borewell）在印度许多地区是反复发生的悲剧。钻井深度可达数百英尺，直径往往不足一英尺，一旦井口未加盖，幼童便可能滑入其中，被困在成年人无法触及的深度。传统救援方式涉及使用重型机械挖掘一个平行坑道，这可能耗时超过一天，并使儿童面临缺氧、低温、土壤坍塌和脱水等风险。本文提出一种配备实时视频监控的自主钻井救援机器人，旨在缩短这一延误。所提出的机器人是一个紧凑的圆柱形装置，通过地面绞车电机驱动的钢缆下放至钻井中。它搭载红外照明摄像头、温湿度传感器、用于检测氧气耗尽和有害气体的气体传感器、用于测距的超声波传感器以及用于聆听儿童声音的麦克风。机器人上的树莓派（Raspberry Pi）负责视频采集和流媒体传输，而Arduino或ESP32则控制夹爪电机、腿式井壁支撑装置和传感器采样。实时视频和传感器数据通过屏蔽电缆或Wi-Fi链路发送至地面控制站。机器人以受控速度自主下降，利用超声波传感器和摄像头在儿童上方安全距离处停止。其抓壁腿撑开抵住钻井壁以稳定机身，随后在操作员监督下将一组带衬垫的机械臂定位在儿童周围。可沿缆绳下放供氧管，双向音频通道允许父母或救援人员与儿童通话，在整个操作过程中使儿童保持平静。所有传感器读数，包括深度、温度、气体浓度以及各执行器的状态，均显示在地面的笔记本电脑或平板电脑仪表板上，并通过移动网络推送至云服务器。地区官员和远程专家可通过网页查看实时视频流和读数，从而支持消防部门、灾害响应团队和医务人员之间的协调。当氧气降至安全水平以下或夹爪力度超过设定限值时，系统会自动生成警报。该系统旨在为受过训练的救援人员提供支持，在关键提升阶段不得在无人监督的情况下自行行动。在带有配重的垂直PVC管模型上进行的测试","International Journal of Data Science and IoT Management System",{"impact":119,"substance":119,"depth":119,"authority":119,"freshness":119,"relevant":119,"comment":960},"该论文聚焦井下救援机器人，与三农、农业信息化、智慧农业等主题无关，不建议进入每日精选。",[962],{"name":958,"url":955},[34,964,965,966],"传感器网络","应急救援机器人","视频监控",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":969},{"candidates":970,"agricultural_evidence":22,"reason":347},[971,975,979],{"code":197,"score":87,"supported":51,"title":972,"abstract":973,"tags":974},[],[200,201],[],{"code":50,"score":205,"supported":51,"title":976,"abstract":977,"tags":978},[],[59],[],{"code":62,"score":8,"supported":22,"title":980,"abstract":981,"tags":982},[],[],[34],[984,985],"应急救援机器人 传感器网络 视频监控 物联网","应急救援机器人 传感器网络","应急救援机器人传感器网络视频监控物联网-4666",{"doi":17,"openalex_id":988,"authors":989,"venue":958,"cited_by_count":119,"oa_url":992,"card":993,"direction":128,"ingested_from":130},"W7221205828",[990],{"name":991,"orcid":17},"Mrs.Sunanda Kondapalli,  Bokka Manoj Kumar,  Suddula Sushanth,  Aluwala Kavya Sri, Vorsa Lokesh, Barige Anusha","https:\u002F\u002Fijdim.com\u002Fjournal\u002Findex.php\u002Fijdim\u002Farticle\u002Fdownload\u002F1442\u002F1374",{"tldr":994,"method":995,"finding":996,"direction":997,"opportunity":998},"研发自主钻孔救援机器人，带实时视频与传感器，辅助营救坠井儿童。","Raspberry Pi视频流、Arduino\u002FESP32控制、多传感器融合、云","机器人可自主下放、稳定并定位儿童，实时传输数据，支持远程协同救援。","其他","可探索多传感器融合的自主决策与安全抓取策略，提升复杂井况下的救援成功率。","2026-10-11T23:30:14.724234Z",{"id":1001,"title":1002,"url":1003,"summary":1004,"summary_zh":1005,"content":17,"source_name":1006,"source_url":1003,"published_at":1007,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1008,"score_detail":1009,"sources":1011,"tags":1013,"primary_topic":1015,"secondary_topics":1021,"classification":1024,"search_phrases":1053,"slug":1056,"view_count":8,"doi":1057,"paper":1058,"created_at":1071},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":174,"substance":252,"depth":26,"authority":251,"freshness":142,"relevant":8,"comment":1010},"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[1012],{"name":1006,"url":1003},[32,33,34,82,1014],"棉花病害",{"code":1016,"slug":1017,"name":1018,"path":1019,"parent_code":1020},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[1022,1023],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1025},{"candidates":1026,"agricultural_evidence":51},[1027,1031,1037,1041,1045,1049],{"code":62,"score":397,"supported":51,"title":1028,"abstract":1029,"tags":1030},[4],[34,4],[34],{"code":1016,"score":498,"supported":51,"title":1032,"abstract":1034,"tags":1036},[1033],"cotton",[1035,1033],"棉花",[],{"code":78,"score":835,"supported":51,"title":1038,"abstract":1039,"tags":1040},[],[32,82,83],[32,82],{"code":50,"score":21,"supported":51,"title":1042,"abstract":1043,"tags":1044},[],[55,56,53,58,59],[33],{"code":69,"score":87,"supported":51,"title":1046,"abstract":1047,"tags":1048},[],[72,74],[],{"code":197,"score":87,"supported":51,"title":1050,"abstract":1051,"tags":1052},[],[200,201],[],[1054,1055],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":1057,"openalex_id":1059,"authors":1060,"venue":1006,"cited_by_count":119,"oa_url":1003,"card":1066,"direction":128,"ingested_from":130},"W7221340722",[1061,1063],{"name":1062,"orcid":17},"Deepak R Khadse",{"name":1064,"orcid":1065},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":1067,"method":1068,"finding":1069,"direction":128,"opportunity":1070},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":1073,"title":1074,"url":1075,"summary":1076,"summary_zh":1077,"content":17,"source_name":18,"source_url":1075,"published_at":1007,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":818,"score_detail":1078,"sources":1080,"tags":1082,"primary_topic":1085,"secondary_topics":1086,"classification":1088,"search_phrases":1115,"slug":1118,"view_count":119,"doi":1119,"paper":1120,"created_at":1128},4658,"Energy-Efficient Smart Agriculture Management Framework Using IoT and Ensemble Machine Learning for Intelligent Crop Health Monitoring and Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.62s.32","Context: Smart agriculture plays an important role in addressing the challenges of food security, environmental sustainability, and sustainable farming through IoT.Farmers are facing many issues in traditional farming, such as inefficient monitoring and poor crop health detection.Traditional farming has a negative impact on crop production and sustainability.In this regard, the development of intelligent and energy-efficient agricultural monitoring systems has become crucial for contemporary precision farming applications.Objective: This study aims to monitor crop health and optimize resource use using machine learning techniques.Our proposed system intends to monitor crop conditions in real time, enhance agricultural management, and minimize unnecessary sensor utilization and communication overhead.Material\u002Fmethods: In this paper, we used five machine learning classifiers to monitor crop health using sensor data.The agricultural sensor data (soil moisture, soil temperature, soil pH, humidity, air temperature, solar radiation, wind speed, NDVI index, and 5G latency) were used for crop health monitoring.The models, such as RF, XGBoost, LightGBM, and CatBoost, and soft voting were used for crop health classification into the Healthy, High_Stress, and Moderate_Stress categories.The experimental observation revealed that soft voting achieved the highest accuracy of 94.5%, macro-F1 score of 0.945, weighted F1 score of 0.945, and AUC-ROC score of 0.988 compared to the other models.The cross-validation results further validated the robustness and stability of the proposed framework.The IoTenabled intelligent agriculture management system offers an efficient, scalable solution for real-time crop health monitoring, energy-efficient resource optimization, and sustainable smart farming.","背景：智慧农业在借助物联网（IoT）应对粮食安全、环境可持续性和可持续耕作等挑战方面发挥着重要作用。农民在传统耕作中面临诸多问题，例如监测效率低下和作物健康检测不佳。传统耕作对作物产量和可持续性产生负面影响。在此背景下，开发智能且节能的农业监测系统对于当代精准农业应用已变得至关重要。目标：本研究旨在利用机器学习技术监测作物健康并优化资源利用。我们提出的系统旨在实时监测作物状况、增强农业管理并最大限度减少不必要的传感器使用和通信开销。材料\u002F方法：在本文中，我们使用了五种机器学习分类器，利用传感器数据监测作物健康。农业传感器数据（土壤湿度、土壤温度、土壤pH值、湿度、空气温度、太阳辐射、风速、NDVI指数和5G延迟）被用于作物健康监测。RF、XGBoost、LightGBM和CatBoost等模型以及软投票被用于将作物健康分类为健康、高胁迫和中等胁迫类别。实验观察表明，与其他模型相比，软投票达到了最高的准确率94.5%、宏F1分数0.945、加权F1分数0.945和AUC-ROC分数0.988。交叉验证结果进一步验证了所提出框架的鲁棒性和稳定性。该物联网赋能的智能农业管理系统为实时作物健康监测、节能资源优化和可持续智慧农业提供了一种高效、可扩展的解决方案。",{"impact":27,"substance":174,"depth":498,"authority":87,"freshness":27,"relevant":8,"comment":1079},"方法组合与多源传感器数据有一定新意，但期刊权威性弱、属实验室验证，公共价值有限，不宜进入每日精选。",[1081],{"name":18,"url":1075},[32,33,34,1083,1084],"作物健康监测","资源优化",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[1087],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1089},{"candidates":1090,"agricultural_evidence":51},[1091,1095,1100,1104,1111],{"code":78,"score":24,"supported":51,"title":1092,"abstract":1093,"tags":1094},[461],[32,82,461],[32],{"code":62,"score":24,"supported":51,"title":1096,"abstract":1097,"tags":1099},[4],[34,1098,4],"农业传感",[34],{"code":50,"score":397,"supported":51,"title":1101,"abstract":1102,"tags":1103},[171],[170,171],[33],{"code":1105,"score":87,"supported":51,"title":1106,"abstract":1107,"tags":1110},"A8",[],[1108,1109],"粮食安全","food security",[],{"code":197,"score":87,"supported":51,"title":1112,"abstract":1113,"tags":1114},[],[200,201],[],[1116,1117],"IoT 作物健康监测","集成学习 精准农业","IoT作物健康监测-4658","10.25258\u002Fijddt.16.62s.32",{"doi":1119,"openalex_id":1121,"authors":1122,"venue":18,"cited_by_count":119,"oa_url":1075,"card":1123,"direction":128,"ingested_from":130},"W7221334384",[],{"tldr":1124,"method":1125,"finding":1126,"direction":128,"opportunity":1127},"用IoT传感器数据与集成机器学习实现作物健康分类与资源优化。","五种分类器（RF、XGBoost、LightGBM、CatBoost、软投票）结","软投票集成模型准确率达94.5%，AUC-ROC为0.988，优于单一模型。","可探索边缘端轻量化集成模型与能耗-精度联合优化，降低传感器通信开销。","2026-10-11T23:30:07.636015Z",{"id":1130,"title":1131,"url":1132,"summary":1133,"summary_zh":1134,"content":17,"source_name":1006,"source_url":1132,"published_at":1007,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":7,"score_detail":1135,"sources":1137,"tags":1139,"primary_topic":1141,"secondary_topics":1145,"classification":1146,"search_phrases":1173,"slug":1176,"view_count":8,"doi":1177,"paper":1178,"created_at":1192},4655,"Twin Grow: A Cloud-Based AIoT Digital Twin System for Intelligent Mushroom Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23276350","Growing of mushrooms demands a close regulation of climate conditions including temperature, humidity, CO 2 level, light intensity, and moisture in the substratum. Nevertheless, a lot of farms still use manual control and reactive changes, which result in irregular production and the waste of resources. In a bid to overcome such issues, the current paper presents TwinGrow, a cloud-based AIoT Digital Twin that will help to monitor the environment of mushroom cultivation in real-time and predict as well as control its conditions automatically. The system will have a combination of IoT sensors like DHT22, MQ-135, LDR, and soil moisture sensors and ESP32 microcontroller as the edge controller. Sensor data is sent to AWSiot core, placed in dynamoDB and run on machine learning models with AWS sage maker. Digital Twin developed using AWS TwinMaker is a visualization and predictive environmental will replica of the cultivation chamber. The controlled use of mist generators, fans, and pumps, on a closed-loop system, are controlled by automated applications using AWS Lambda and Node-RED. The efficacy of AIoT-oriented smart agriculture using experimental outcomes indicates better environmental conditions, low water and energy usage, and predictable yields.","蘑菇种植需要对气候条件进行严格调控，包括温度、湿度、二氧化碳（CO₂）浓度、光照强度以及基质含水量。然而，许多农场仍采用人工控制和被动式调节，导致产量不稳定和资源浪费。为解决这些问题，本文提出了TwinGrow，一种基于云的AIoT数字孪生系统，可实时监测蘑菇栽培环境，并自动预测和调控其条件。该系统将DHT22、MQ-135、LDR和土壤湿度传感器等物联网传感器与ESP32微控制器作为边缘控制器相结合。传感器数据发送至AWS IoT Core，存储于DynamoDB，并通过AWS SageMaker上的机器学习模型运行。利用AWS TwinMaker开发的数字孪生是栽培室的可视化与预测性环境副本。在闭环系统中，喷雾发生器、风扇和水泵的受控使用由基于AWS Lambda和Node-RED的自动化应用程序进行管理。面向AIoT的智慧农业的实验结果表明，其有效性体现在环境条件改善、水和能源消耗降低以及产量可预测等方面。",{"impact":174,"substance":616,"depth":26,"authority":141,"freshness":27,"relevant":8,"comment":1136},"提出基于云AIoT数字孪生的蘑菇种植环境实时监测与自动调控系统，方法新颖、实验结论明确，对智慧农业细分场景有参考价值。",[1138],{"name":1006,"url":1132},[32,33,34,345,1140],"食用菌",{"code":1142,"slug":1143,"name":1140,"path":1144,"parent_code":563},"B5","mushrooms","\u002Ftopic\u002Fhorticulture\u002Fmushrooms",[],{"status":164,"locked":22,"version":45,"method":46,"evidence":1147},{"candidates":1148,"agricultural_evidence":51},[1149,1155,1161,1165,1169],{"code":78,"score":1150,"supported":51,"title":1151,"abstract":1153,"tags":1154},19,[1152],"digital twin",[32,345,461,1152],[345,32],{"code":1142,"score":397,"supported":51,"title":1156,"abstract":1158,"tags":1160},[1157],"mushroom",[1159,1157],"蘑菇",[1140],{"code":50,"score":79,"supported":51,"title":1162,"abstract":1163,"tags":1164},[],[170,171],[33],{"code":62,"score":79,"supported":51,"title":1166,"abstract":1167,"tags":1168},[],[34,4],[34],{"code":197,"score":87,"supported":51,"title":1170,"abstract":1171,"tags":1172},[],[200,201],[],[1174,1175],"TwinGrow 蘑菇 数字孪生","AIoT 食用菌 智能种植","TwinGrow蘑菇数字孪生-4655","10.5281\u002Fzenodo.23276350",{"doi":1177,"openalex_id":1179,"authors":1180,"venue":1006,"cited_by_count":119,"oa_url":1132,"card":1187,"direction":128,"ingested_from":130},"W7221319111",[1181,1183,1185],{"name":1182,"orcid":17},"Katam Reddy Charan Reddy",{"name":1184,"orcid":17},"Ajjaguttu Abhishikth Reddy",{"name":1186,"orcid":17},"Dr. A. Anbarasi",{"tldr":1188,"method":1189,"finding":1190,"direction":128,"opportunity":1191},"提出TwinGrow云AIoT数字孪生系统，实现蘑菇栽培环境实时监测与自动调控。","ESP32与多传感器采集数据，经AWS IoT、SageMaker和TwinMa","系统改善环境条件，降低水耗与能耗，并实现可预测的蘑菇产量。","可探索数字孪生与作物生长模型的耦合，实现产量与品质的精准预测及跨品种迁移。","2026-10-11T23:30:07.397887Z",{"id":1194,"title":1195,"url":1196,"summary":1197,"summary_zh":1198,"content":17,"source_name":1199,"source_url":1196,"published_at":1200,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1201,"score_detail":1202,"sources":1205,"tags":1207,"primary_topic":1211,"secondary_topics":1212,"classification":1214,"search_phrases":1241,"slug":1244,"view_count":8,"doi":17,"paper":1245,"created_at":1255},4578,"Automatic irrigation system-1","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4718","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soil-moisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成严重的水资源浪费。传统灌溉方式往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到充足水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化在提高用水效率方面具有潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。I 较新的系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来改进灌溉调度。Wiley Online Library+1 因此，该系统是一种低成本且可扩展的方法，有助于实现精准农业、节水、减少劳动力需求以及可持续农业生产。","International Journal of Engineering Research and Science & Technology","2026-10-07T00:00:00Z",34,{"impact":87,"substance":27,"depth":142,"authority":1203,"freshness":87,"relevant":8,"comment":1204},5,"论文仅泛述土壤湿度传感+单片机+水泵的自动灌溉原理，方法无新颖性、无实验数据，来源为普通工程期刊，公共价值有限，不建议进入每日精选。",[1206],{"name":1199,"url":1196},[32,34,1208,1209,1210],"土壤墒情","节水农业","自动灌溉",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[1213],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1215},{"candidates":1216,"agricultural_evidence":51},[1217,1221,1225,1229,1233,1237],{"code":158,"score":24,"supported":51,"title":1218,"abstract":1219,"tags":1220},[159],[186,187,159],[1209],{"code":62,"score":21,"supported":51,"title":1222,"abstract":1223,"tags":1224},[],[34,65,4],[34],{"code":197,"score":79,"supported":51,"title":1226,"abstract":1227,"tags":1228},[],[200,201],[1208],{"code":78,"score":79,"supported":51,"title":1230,"abstract":1231,"tags":1232},[],[82,83],[32],{"code":50,"score":87,"supported":51,"title":1234,"abstract":1235,"tags":1236},[],[170,171],[],{"code":926,"score":205,"supported":51,"title":1238,"abstract":1239,"tags":1240},[],[929],[],[1242,1243],"自动灌溉系统 土壤湿度传感器","IoT 精准灌溉 节水","自动灌溉系统土壤湿度传感器-4578",{"doi":17,"openalex_id":1246,"authors":1247,"venue":1199,"cited_by_count":119,"oa_url":1196,"card":1250,"direction":128,"ingested_from":130},"W7221294404",[1248],{"name":1249,"orcid":17},"Pooja Rajendra Sangole,Shravani Datta Dhomane ,Shreyas nivrutti thete ,amol Dinakar laware,Sumit sudan dalu,Kalpesh Gopal Agrawal,Piyush Sitaram Ghore",{"tldr":1251,"method":1252,"finding":1253,"direction":128,"opportunity":1254},"设计基于土壤湿度传感、微控制器和继电器的自动灌溉系统，按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可扩展IoT远程监控。","基于传感器的自动灌溉可提升水分利用效率，灌溉效率可达86.6%。","可结合气象数据与机器学习优化灌溉阈值，并开展田间长期节水增产验证。","2026-10-10T23:30:15.205190Z",{"id":1257,"title":1258,"url":1259,"summary":1260,"summary_zh":1261,"content":17,"source_name":1199,"source_url":1259,"published_at":1200,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1262,"score_detail":1263,"sources":1265,"tags":1267,"primary_topic":1268,"secondary_topics":1269,"classification":1271,"search_phrases":1298,"slug":1301,"view_count":205,"doi":17,"paper":1302,"created_at":1311},4577,"Automatic irrigation system-2","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4719","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soilmoisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成大量水资源浪费。传统灌溉方法往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度水平。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到足够水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化具有提高水资源利用效率的潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。近期系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来优化灌溉调度。因此，所提出的系统是一种低成本、可扩展的方案，有助于实现精准农业、水资源保护、减少劳动力需求以及可持续农业生产。",42,{"impact":27,"substance":21,"depth":251,"authority":87,"freshness":87,"relevant":8,"comment":1264},"主题契合智慧农业，但属通用性论文介绍，方法无新意、来源权威性一般，不建议进入每日精选。",[1266],{"name":1199,"url":1259},[32,34,82,1208,1210],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[1270],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1272},{"candidates":1273,"agricultural_evidence":51},[1274,1278,1282,1286,1290,1294],{"code":158,"score":26,"supported":51,"title":1275,"abstract":1276,"tags":1277},[159],[186,187,159],[],{"code":62,"score":21,"supported":51,"title":1279,"abstract":1280,"tags":1281},[],[34,65,4],[34],{"code":78,"score":27,"supported":51,"title":1283,"abstract":1284,"tags":1285},[],[82,83],[32,82],{"code":197,"score":79,"supported":51,"title":1287,"abstract":1288,"tags":1289},[],[200,201],[1208],{"code":50,"score":87,"supported":51,"title":1291,"abstract":1292,"tags":1293},[],[170,171],[],{"code":926,"score":205,"supported":51,"title":1295,"abstract":1296,"tags":1297},[],[929],[],[1299,1300],"自动灌溉系统 土壤湿度 物联网","土壤墒情 智慧农业 精准农业 自动灌溉","自动灌溉系统土壤湿度物联网-4577",{"doi":17,"openalex_id":1303,"authors":1304,"venue":1199,"cited_by_count":119,"oa_url":1259,"card":1306,"direction":128,"ingested_from":130},"W7221285464",[1305],{"name":1249,"orcid":17},{"tldr":1307,"method":1308,"finding":1309,"direction":128,"opportunity":1310},"设计基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统，实现按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可选IoT远程监控。","系统在湿度低于阈值时自动启泵，恢复后停泵，低成本可扩展，提升水分利用效率。","可结合机器学习与气象数据优化灌溉阈值，并开展多作物田间验证与能耗优化。","2026-10-10T23:30:15.132034Z",{"id":1313,"title":1314,"url":1315,"summary":1316,"summary_zh":1317,"content":17,"source_name":1318,"source_url":1315,"published_at":1319,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":551,"score_detail":1320,"sources":1322,"tags":1324,"primary_topic":1326,"secondary_topics":1327,"classification":1328,"search_phrases":1359,"slug":1362,"view_count":119,"doi":1363,"paper":1364,"created_at":1382},4572,"Design and development of IoT based solar power agriculture robot","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44430-026-00040-6","Agriculture in developing economies such as Pakistan remains heavily dependent on manual labor and single-purpose, grid or fuel-powered machinery that is often unaffordable for smallholder farmers. This paper presents the design, fabrication, and experimental evaluation of a multifunctional, solar-powered, Internet of Things (IoT)-enabled agricultural robot capable of performing soil cultivation, seed placement, irrigation, and crop cutting within a single integrated platform. The system architecture is built around an ESP32 microcontroller that interfaces with a DHT11 temperature\u002Fhumidity sensor, an HC-SR04 ultrasonic obstacle sensor, an ESP32-CAM module for visual field monitoring, an L298N dual H-bridge motor driver, and relay-controlled actuators, with energy supplied by a 20 W photovoltaic panel, a 12 V\u002F7.2 Ah battery, and a battery management system (BMS). Mechanical subsystems comprising a tooth-type cultivator, a soil-leveling roller, a servo-actuated seed dispenser, a DC pump-based irrigation unit, and a blade-type cutter were designed in CAD, fabricated, and integrated onto a four-wheel-drive chassis. Engineering calculations were used to size the power budget, battery backup, drive-motor torque, structural factor of safety, seeding rate, and irrigation flow. Field trials show a typical operating power demand of 24.4 W against an average usable solar output of 11–12 W, a battery backup of approximately 3.5 h, The experimental results demonstrated satisfactory performance of the proposed agricultural robot, achieving an average seed-placement accuracy of 74.45% over 11 experimental trials. The developed system also successfully performed soil digging, irrigation, crop cutting, obstacle detection, and real-time IoT-based remote monitoring, demonstrating its suitability for sustainable precision agriculture.","巴基斯坦等发展中经济体的农业仍然高度依赖人工劳动以及单一用途的电网或燃料驱动机械，而这类机械往往是小农户无力承担的。本文介绍了一种多功能、太阳能供电、支持物联网（IoT）的农业机器人的设计、制造与实验评估，该机器人能够在单一集成平台上完成土壤耕作、播种、灌溉和作物收割。系统架构以ESP32微控制器为核心，连接DHT11温湿度传感器、HC-SR04超声波避障传感器、用于田间视觉监测的ESP32-CAM模块、L298N双H桥电机驱动器以及继电器控制执行器，能源由20 W光伏板、12 V\u002F7.2 Ah电池和电池管理系统（BMS）提供。机械子系统包括齿式耕作器、平土辊、舵机驱动排种器、基于直流泵的灌溉单元和刀片式切割器，经CAD设计、制造并集成到四轮驱动底盘上。通过工程计算确定了功率预算、电池续航、驱动电机扭矩、结构安全系数、播种速率和灌溉流量。田间试验表明，典型工作功率需求为24.4 W，而平均可用太阳能输出为11–12 W，电池续航约为3.5 h。实验结果表明，所提出的农业机器人性能令人满意，在11次实验 trials 中实现了74.45%的平均播种精度。所开发的系统还成功完成了挖土、灌溉、作物切割、障碍物检测以及基于物联网的实时远程监测，表明其适用于可持续精准农业。","Discover Robotics","2026-10-09T00:00:00Z",{"impact":27,"substance":24,"depth":397,"authority":87,"freshness":142,"relevant":8,"comment":1321},"面向小农户的多功能太阳能物联网农业机器人论文，方法集成度高、有实测数据，但属单篇学术成果，公共影响有限，可作为智慧农业技术前沿收录。",[1323],{"name":1318,"url":1315},[32,824,34,82,1325],"光伏农业",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":1329},{"candidates":1330,"agricultural_evidence":51},[1331,1335,1339,1344,1351,1355],{"code":62,"score":24,"supported":51,"title":1332,"abstract":1333,"tags":1334},[4],[34,65,4],[34],{"code":78,"score":27,"supported":51,"title":1336,"abstract":1337,"tags":1338},[],[82,83],[32,82],{"code":852,"score":79,"supported":51,"title":1340,"abstract":1341,"tags":1343},[],[824,1342],"agricultural robot",[824],{"code":1345,"score":87,"supported":51,"title":1346,"abstract":1347,"tags":1350},"G2",[],[1348,1349],"小农户","smallholder",[],{"code":197,"score":87,"supported":51,"title":1352,"abstract":1353,"tags":1354},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":1356,"abstract":1357,"tags":1358},[],[186,159],[],[1360,1361],"太阳能农业机器人 ESP32","IoT 农业机器人 播种精度","太阳能农业机器人ESP32-4572","10.1007\u002Fs44430-026-00040-6",{"doi":1363,"openalex_id":1365,"authors":1366,"venue":1318,"cited_by_count":119,"oa_url":1315,"card":1377,"direction":128,"ingested_from":130},"W7221126513",[1367,1369,1371,1373,1375],{"name":1368,"orcid":17},"Mohammad Ali",{"name":1370,"orcid":17},"Sana Ullah",{"name":1372,"orcid":17},"Asad Ullah",{"name":1374,"orcid":17},"M. Danyal Khan",{"name":1376,"orcid":17},"Mubasher Hassan",{"tldr":1378,"method":1379,"finding":1380,"direction":128,"opportunity":1381},"设计并试验了一款太阳能物联网多功能农业机器人，可耕地、播种、灌溉和收割。","基于ESP32、DHT11、超声波、ESP32-CAM和20W光伏板，CAD制造","作业功耗24.4W，电池续航约3.5小时，11次试验平均播种精度74.45%。","太阳能供电与作业功耗不匹配，可研究能量优化调度或混合供电提升续航与播种精度。","2026-10-10T23:30:13.752841Z",{"id":1384,"title":1385,"url":1386,"summary":1387,"summary_zh":1388,"content":17,"source_name":1389,"source_url":1386,"published_at":887,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":378,"score_detail":1390,"sources":1392,"tags":1394,"primary_topic":1397,"secondary_topics":1398,"classification":1399,"search_phrases":1426,"slug":1429,"view_count":119,"doi":1430,"paper":1431,"created_at":1451},4568,"Design and Controlled-Plot Validation of a Smart Dual-Mode Irrigation System for West African Riverbank Farms","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1019-6198","Ewedu (Corchorus olitorius) is a nutritionally and economically vital leafy vegetable in West Africa, yet dry-season cultivation on riverbank plots remains constrained by manual, intuition-driven irrigation that is labour-intensive and agronomically imprecise. Existing smart irrigation systems are poorly suited to this setting, as they typically assume mains electricity, reliable internet, and technically skilled users conditions largely absent on West African riverbank farms. To address this gap, we designed, built, and field-tested a low-cost, solar-powered IoT irrigation system that uses real-time capacitive soil moisture monitoring to automate ewedu irrigation for smallholder farmers. The system integrates an ESP32 microcontroller running crop-specific hysteresis logic, a 12V DC pump and micro-sprinkler network powered directly from a solar-charged battery bank (eliminating the inverter stage), and a farmer-friendly web dashboard usable both online and offline. Gravimetric calibration of the moisture sensor achieved a mean R² of 0.975. Field testing established an irrigation hysteresis band of 22–25% volumetric water content (VWC) for pump activation and 35–38% VWC for deactivation. Over a 42-day trial, the solar subsystem maintained a mean battery state of charge of 50%, with no low-battery events, against average daily consumption of 40 Wh. A recruited farmer completed all seven user-acceptance tasks unaided, rating ease of use at 4.6\u002F5.","长蒴黄麻（Ewedu，学名Corchorus olitorius）是西非一种在营养和经济上均至关重要的叶菜，然而其在河岸地块上的旱季种植仍受限于人工、凭直觉驱动的灌溉方式，这种方式劳动强度大且在农艺上不够精确。现有的智能灌溉系统难以适用于这一场景，因为它们通常假定具备市电供电、可靠的互联网以及技术熟练的用户——而这些条件在西非河岸农场中基本不存在。为弥补这一空白，我们设计、搭建并田间测试了一套低成本、太阳能供电的物联网灌溉系统，利用实时电容式土壤水分监测为小农户实现长蒴黄麻灌溉的自动化。该系统集成了一个运行作物专用滞回逻辑的ESP32微控制器、一套直接由太阳能充电电池组供电的12V直流泵和微喷灌网络（省去了逆变器环节），以及一个农民友好、可在线和离线使用的网页仪表盘。水分传感器的重量法校准取得了平均R²为0.975的结果。田间测试确定了灌溉滞回区间：体积含水量（VWC）22–25%时启动水泵，35–38%时关闭水泵。在为期42天的试验中，太阳能子系统在日均耗电40 Wh的情况下，将电池平均荷电状态维持在50%，且未出现低电量事件。一名招募的农民在无人协助下完成了全部七项用户验收任务，并将易用性评为4.6\u002F5。","FUDMA Journal of Sciences",{"impact":27,"substance":616,"depth":26,"authority":251,"freshness":142,"relevant":8,"comment":1391},"面向西非河岸小农户的低成本太阳能物联网灌溉系统，含实地验证数据，方法扎实但影响范围限于区域。",[1393],{"name":1389,"url":1386},[32,34,503,1395,1396],"太阳能","西非农业",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":1400},{"candidates":1401,"agricultural_evidence":51},[1402,1406,1410,1414,1418,1422],{"code":158,"score":397,"supported":51,"title":1403,"abstract":1404,"tags":1405},[159],[186,159],[503],{"code":62,"score":79,"supported":51,"title":1407,"abstract":1408,"tags":1409},[],[34,4],[34],{"code":1345,"score":87,"supported":51,"title":1411,"abstract":1412,"tags":1413},[],[1348,1349],[],{"code":197,"score":87,"supported":51,"title":1415,"abstract":1416,"tags":1417},[],[200,201],[],{"code":108,"score":205,"supported":51,"title":1419,"abstract":1420,"tags":1421},[],[112],[],{"code":78,"score":8,"supported":22,"title":1423,"abstract":1424,"tags":1425},[],[],[32],[1427,1428],"ESP32 太阳能灌溉 西非","Ewedu 土壤湿度 灌溉系统","ESP32太阳能灌溉西非-4568","10.33003\u002Ffjs-2026-1019-6198",{"doi":1430,"openalex_id":1432,"authors":1433,"venue":1389,"cited_by_count":119,"oa_url":1445,"card":1446,"direction":128,"ingested_from":130},"W7221041404",[1434,1437,1440,1442],{"name":1435,"orcid":1436},"Izuchukwu Okafor","https:\u002F\u002Forcid.org\u002F0009-0003-8668-3155",{"name":1438,"orcid":1439},"Afeez Adewale","https:\u002F\u002Forcid.org\u002F0009-0009-7028-9237",{"name":1441,"orcid":17},"Olawale Olugbenga Olarewaju",{"name":1443,"orcid":1444},"Mobolaji Oluyimika Omobowale","https:\u002F\u002Forcid.org\u002F0000-0002-7108-7188","https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F6198\u002F4077",{"tldr":1447,"method":1448,"finding":1449,"direction":128,"opportunity":1450},"为西非河岸农场设计并验证低成本太阳能物联网灌溉系统，自动灌溉黄麻叶。","ESP32、电容式土壤湿度传感器、太阳能直驱水泵、离线网页仪表盘，42天田间试验","湿度传感器R²达0.975，灌溉阈值22-25%启动、35-38%停止，电池无低压事件，农户易用性4","可探索无电网、无网络环境下低成本IoT灌溉的作物通用化与长期可靠性验证。","2026-10-10T23:30:11.359371Z",{"id":1453,"title":1454,"url":1455,"summary":1456,"summary_zh":1457,"content":17,"source_name":1458,"source_url":1455,"published_at":1319,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1459,"score_detail":1460,"sources":1462,"tags":1464,"primary_topic":1467,"secondary_topics":1468,"classification":1469,"search_phrases":1490,"slug":1493,"view_count":119,"doi":1494,"paper":1495,"created_at":1510},4564,"Enhancing Long-Tailed Macaque Pest Identification Using a Two-Stage YOLO-EfficientNet Pipeline","https:\u002F\u002Fdoi.org\u002F10.36456\u002F1hrtgc63","Long-tailed macaque (Macaca fascicularis) infestations have reduced corn production from a potential 7–9 tons to only 4–5 tons per growing season (±40%), while conventional deterrence methods such as loud noise and shooting have proven ineffective. This study developed an Internet of Things (IoT)-based pest repellent system using audiosonic waves, an HC-SR501 sensor, and an ESP32-CAM module, incorporating YOLOv8 and EfficientNet-B0 to replace the previously used YOLOv3 Tiny model, with the aim of improving detection accuracy and distinguishing long-tailed macaques from humans. Since population density at the partner's farmland was insufficient to construct an adequate dataset, image data were collected at Kembang Island, South Kalimantan, which serves as a natural habitat supporting a large and observable population of long-tailed macaques, thereby enabling the acquisition of visually representative and diverse training data. A total of 3,083 images were annotated using Label Studio under two classes (human and long-tailed macaque), then divided at an 80:10:10 ratio without augmentation. The YOLOv8n model was trained for 50 epochs, after which validation detections were cropped into 920 images to fine-tune EfficientNet-B0 over 10 epochs. Evaluation results show that YOLOv8 achieved a precision of 0.959, recall of 0.965, mAP50 of 0.979, and mAP50-95 of 0.723, while EfficientNet-B0 reached validation accuracies of 98.4–100% with no significant overfitting. The two-stage detection-classification pipeline was successfully tested end-to-end on 121 field images, demonstrating substantial performance improvements over the prior model and contributing toward IoT and AI-based smart agriculture solutions for effective, sustainable corn field protection.","长尾猕猴（Macaca fascicularis）的侵害使玉米产量从潜在的每生长季7–9吨降至仅4–5吨（±40%），而诸如大声喧哗和射击等传统驱赶方法已被证明无效。本研究开发了一种基于物联网（IoT）的驱虫系统，采用声波、HC-SR501传感器和ESP32-CAM模块，并引入YOLOv8和EfficientNet-B0以替代先前使用的YOLOv3 Tiny模型，旨在提高检测精度并区分长尾猕猴与人类。由于合作农田的种群密度不足以构建足够的数据集，图像数据采集于南加里曼丹的Kembang岛，该岛作为自然栖息地，拥有大量且可观察的长尾猕猴种群，从而能够获取具有视觉代表性和多样性的训练数据。使用Label Studio对共计3,083张图像进行标注，分为两个类别（人类和长尾猕猴），然后按80:10:10的比例划分，未进行数据增强。YOLOv8n模型训练了50个epoch，之后将验证检测结果裁剪为920张图像，用于对EfficientNet-B0进行10个epoch的微调。评估结果表明，YOLOv8达到了0.959的精确率、0.965的召回率、0.979的mAP50和0.723的mAP50-95，而EfficientNet-B0达到了98.4–100%的验证准确率，且无明显过拟合。该两阶段检测-分类流水线在121张实地图像上成功进行了端到端测试，展示出相较于先前模型的显著性能提升，为基于物联网和人工智能的智慧农业解决方案做出了贡献，可有效、可持续地保护玉米田。","BEST Journal of Applied Electrical Science & Technology",69,{"impact":251,"substance":25,"depth":26,"authority":142,"freshness":142,"relevant":8,"comment":1461},"论文提出YOLOv8+EfficientNet-B0两阶段猕猴识别与声波驱避系统，数据规模与指标扎实，对智慧农业AI应用有参考价值。",[1463],{"name":1458,"url":1455},[32,33,34,1465,1466],"目标检测","玉米病虫害",{"code":38,"slug":39,"name":40,"path":41,"parent_code":17},[],{"status":44,"locked":22,"version":45,"method":46,"evidence":1470},{"candidates":1471,"agricultural_evidence":51,"reason":114},[1472,1477,1481,1485],{"code":50,"score":21,"supported":51,"title":1473,"abstract":1475,"tags":1476},[1474],"YOLO",[],[33,1465],{"code":62,"score":21,"supported":51,"title":1478,"abstract":1479,"tags":1480},[],[34,65,4],[34],{"code":78,"score":79,"supported":51,"title":1482,"abstract":1483,"tags":1484},[],[32,461],[32],{"code":94,"score":87,"supported":51,"title":1486,"abstract":1487,"tags":1489},[],[97,1488],"corn",[],[1491,1492],"长尾猕猴 玉米 识别","农业人工智能 玉米病虫害 智慧农业 目标检测","长尾猕猴玉米识别-4564","10.36456\u002F1hrtgc63",{"doi":1494,"openalex_id":1496,"authors":1497,"venue":1458,"cited_by_count":119,"oa_url":1455,"card":1505,"direction":128,"ingested_from":130},"W7221187629",[1498,1500,1503],{"name":1499,"orcid":17},"Hammami Rifqi Darmawan",{"name":1501,"orcid":1502},"Finki Dona Marleny","https:\u002F\u002Forcid.org\u002F0000-0002-3437-5594",{"name":1504,"orcid":17},"Ayu Ahadi Ningrum",{"tldr":1506,"method":1507,"finding":1508,"direction":128,"opportunity":1509},"用YOLOv8+EfficientNet-B0两阶段流水线识别长尾猕猴，提升玉米田驱猴系统检测精度。","3083张标注图像，YOLOv8n检测后裁剪920张微调EfficientNet","YOLOv8 mAP50达0.979，EfficientNet-B0验证准确率98.4–100%，1","可探索将两阶段检测-分类流水线轻量化部署到边缘设备，并扩展至多物种、多作物场景的实时驱避决策。","2026-10-10T23:30:07.974290Z",{"id":1512,"title":1513,"url":1514,"summary":1515,"summary_zh":1516,"content":17,"source_name":1006,"source_url":1514,"published_at":887,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1517,"score_detail":1518,"sources":1520,"tags":1522,"primary_topic":1523,"secondary_topics":1524,"classification":1529,"search_phrases":1553,"slug":1555,"view_count":8,"doi":1556,"paper":1557,"created_at":1567},4491,"INTEGRATING \"SMART AGRICULTURE\" TECHNOLOGIES INTO THE EDUCATIONAL PROCESS IN AGRICULTURAL EDUCATION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23242369","This thesis discusses the pedagogical potential of integrating “Smart Agriculture” technologies into the teaching of specialized subjects at higher agricultural education institutions. Internet of Things (IoT), sensors, digital monitoring, Geographic Information Systems (GIS), remote sensing, and artificial intelligence tools enable students to connect theoretical knowledge with real-world agrotechnological tasks. It is proposed that these technologies should be used in lessons not merely as demonstration tools, but as means of identifying problems, collecting and analyzing data, and making practical decisions.","本论文探讨了将“智慧农业”技术融入高等农业院校专业课程教学的教学潜力。物联网（IoT）、传感器、数字监测、地理信息系统（GIS）、遥感以及人工智能工具能够帮助学生将理论知识与现实农业技术任务相联系。本文提出，这些技术不应仅在课堂上作为演示工具使用，而应作为发现问题、收集与分析数据以及做出实际决策的手段。",64,{"impact":251,"substance":174,"depth":397,"authority":251,"freshness":142,"relevant":8,"comment":1519},"探讨将智慧农业技术融入高等农业教学的论文，属细分领域教育研究，有方法性内容但影响面有限。",[1521],{"name":1006,"url":1514},[32,33,34,636,685],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[1525],{"code":708,"slug":1526,"name":1527,"path":1528,"parent_code":38},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":164,"locked":22,"version":45,"method":46,"evidence":1530},{"candidates":1531,"agricultural_evidence":51},[1532,1536,1540,1545,1549],{"code":78,"score":397,"supported":51,"title":1533,"abstract":1534,"tags":1535},[461],[32,461],[32],{"code":62,"score":21,"supported":51,"title":1537,"abstract":1538,"tags":1539},[],[34,65,4],[34],{"code":708,"score":142,"supported":51,"title":1541,"abstract":1542,"tags":1544},[],[711,712,1543],"geographic information",[],{"code":633,"score":79,"supported":51,"title":1546,"abstract":1547,"tags":1548},[],[636,637],[636],{"code":50,"score":287,"supported":51,"title":1550,"abstract":1551,"tags":1552},[],[58],[33],[1554,719],"智慧农业 农业教育 课程","智慧农业农业教育课程-4491","10.5281\u002Fzenodo.23242369",{"doi":1556,"openalex_id":1558,"authors":1559,"venue":1006,"cited_by_count":119,"oa_url":1514,"card":1562,"direction":128,"ingested_from":130},"W7221022747",[1560],{"name":1561,"orcid":17},"Shamsiyeva Ogiloy",{"tldr":1563,"method":1564,"finding":1565,"direction":734,"opportunity":1566},"探讨将智慧农业技术融入高等农业教育，作为学生解决实际农艺问题的教学工具。","整合物联网、传感器、GIS、遥感与AI工具于专业课程教学。","技术不应仅作演示，而应成为发现问题、采集分析数据并决策的手段。","可研究智慧农业教学对学生数据决策能力的量化影响及课程设计模式。","2026-10-09T23:30:08.054996Z",{"id":1569,"title":1570,"url":1571,"summary":1572,"summary_zh":1573,"content":17,"source_name":1574,"source_url":1571,"published_at":1575,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1576,"score_detail":1577,"sources":1579,"tags":1581,"primary_topic":1584,"secondary_topics":1589,"classification":1592,"search_phrases":1612,"slug":1615,"view_count":406,"doi":1616,"paper":1617,"created_at":1637},4427,"Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges","https:\u002F\u002Fdoi.org\u002F10.56581\u002Fijrerd.11.05.01-08","The development of Internet of Things (IoT) technology provides opportunities for faster and continuous soil monitoring to support precision agriculture.Monitoring soil pH and phosphorus is important in rice cultivation because both parameters are associated with soil chemical conditions and nutrient availability.This review aimed to examine the development of IoT-based soil sensors for monitoring soil pH and phosphorus, including sensor performance, factors affecting measurement results, and potential applications.A literature review was conducted on research and review articles published between 2019 and 2026.The literature was descriptively analyzed and synthesized based on technological development, sensor performance, and environmental and soil characteristics.The results indicate that pH and phosphorus sensors have been developed using various sensing approaches and have demonstrated promising performance under certain conditions; however, measurement results vary across parameters and soil conditions.Sensor performance is influenced by the sensing principle, soil characteristics, environmental conditions, calibration, and validation.IoT-based sensors have the potential to support real-time soil monitoring, but their application as quantitative measurement tools still requires validation and calibration under diverse soil conditions.","物联网（IoT）技术的发展为实现更快、连续性的土壤监测以支持精准农业提供了机遇。监测土壤pH和磷在水稻种植中具有重要意义，因为这两个参数均与土壤化学状况和养分有效性相关。本综述旨在考察用于监测土壤pH和磷的基于物联网的土壤传感器的发展现状，包括传感器性能、影响测量结果的因素以及潜在应用。本文对2019年至2026年间发表的研究论文和综述文章进行了文献综述。基于技术发展、传感器性能以及环境和土壤特征，对文献进行了描述性分析和综合。结果表明，pH和磷传感器已采用多种传感方法进行开发，并在特定条件下展现出良好性能；然而，测量结果因参数和土壤条件的不同而存在差异。传感器性能受传感原理、土壤特性、环境条件、校准和验证的影响。基于物联网的传感器具有支持实时土壤监测的潜力，但其作为定量测量工具的应用仍需在不同土壤条件下进行验证和校准。","International journal of recent engineering research and development.","2026-10-06T00:00:00Z",62,{"impact":251,"substance":24,"depth":397,"authority":142,"freshness":27,"relevant":8,"comment":1578},"综述性论文，系统梳理IoT土壤pH与磷传感器的发展、精度与落地挑战，对智慧农业土壤监测方向有参考价值，但非突破性成果。",[1580],{"name":1574,"url":1571},[32,34,82,1582,1583],"土壤监测","水稻种植",{"code":1585,"slug":1586,"name":1587,"path":1588,"parent_code":1020},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[1590,1591],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":1593},{"candidates":1594,"agricultural_evidence":51},[1595,1599,1604,1608],{"code":62,"score":24,"supported":51,"title":1596,"abstract":1597,"tags":1598},[65],[34,65,4],[34],{"code":1585,"score":397,"supported":51,"title":1600,"abstract":1602,"tags":1603},[1601],"paddy",[1587,1586],[1583],{"code":197,"score":498,"supported":51,"title":1605,"abstract":1606,"tags":1607},[201],[200,201],[],{"code":78,"score":27,"supported":51,"title":1609,"abstract":1610,"tags":1611},[],[82,83],[32,82],[1613,1614],"物联网 土壤传感器 稻田","土壤pH 监测","物联网土壤传感器稻田-4427","10.56581\u002Fijrerd.11.05.01-08",{"doi":1616,"openalex_id":1618,"authors":1619,"venue":1574,"cited_by_count":119,"oa_url":1571,"card":1632,"direction":128,"ingested_from":130},"W7220841659",[1620,1622,1625,1627,1630],{"name":1621,"orcid":17},"Fariha Laqania",{"name":1623,"orcid":1624},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":1626,"orcid":17},"Ade Setiawan",{"name":1628,"orcid":1629},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":1631,"orcid":17},"Wahyu Daradjat",{"tldr":1633,"method":1634,"finding":1635,"direction":128,"opportunity":1636},"综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。","文献综述，按技术发展、传感器性能与土壤环境特征归纳分析。","传感器在特定条件下表现良好，但精度受原理、土壤、环境与校准影响，定量应用仍需验证。","稻田复杂土壤条件下pH\u002F磷传感器的原位校准与多源数据融合验证是待突破的研究空白。","2026-10-08T23:30:13.316611Z",{"id":1639,"title":1640,"url":1641,"summary":1642,"summary_zh":1643,"content":17,"source_name":816,"source_url":1641,"published_at":1200,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1644,"score_detail":1645,"sources":1647,"tags":1649,"primary_topic":1651,"secondary_topics":1652,"classification":1654,"search_phrases":1677,"slug":1680,"view_count":8,"doi":1681,"paper":1682,"created_at":1697},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。",55,{"impact":27,"substance":24,"depth":397,"authority":87,"freshness":27,"relevant":8,"comment":1646},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[1648],{"name":816,"url":1641},[32,33,181,34,1650],"精准灌溉",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[1653],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1655},{"candidates":1656,"agricultural_evidence":51},[1657,1661,1665,1669,1673],{"code":62,"score":835,"supported":51,"title":1658,"abstract":1659,"tags":1660},[],[34,65,4],[34,181],{"code":50,"score":21,"supported":51,"title":1662,"abstract":1663,"tags":1664},[],[55,56,53,59,340],[33],{"code":158,"score":79,"supported":51,"title":1666,"abstract":1667,"tags":1668},[],[186,159],[1650],{"code":78,"score":79,"supported":51,"title":1670,"abstract":1671,"tags":1672},[],[82,83],[32],{"code":197,"score":87,"supported":51,"title":1674,"abstract":1675,"tags":1676},[],[200,201],[],[1678,1679],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":1681,"openalex_id":1683,"authors":1684,"venue":816,"cited_by_count":119,"oa_url":1641,"card":1692,"direction":128,"ingested_from":130},"W7220855887",[1685,1688,1690],{"name":1686,"orcid":1687},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":1689,"orcid":17},"Shetty Chirag",{"name":1691,"orcid":17},"Sunay Kanade",{"tldr":1693,"method":1694,"finding":1695,"direction":128,"opportunity":1696},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":1699,"title":1700,"url":1701,"summary":1702,"summary_zh":1703,"content":17,"source_name":18,"source_url":1701,"published_at":1704,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1705,"score_detail":1706,"sources":1708,"tags":1710,"primary_topic":1712,"secondary_topics":1713,"classification":1714,"search_phrases":1733,"slug":1736,"view_count":406,"doi":1737,"paper":1738,"created_at":1758},4298,"Smart Agriculture Using ESP32","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.48s.39","Agriculture is an important sector that requires continuous monitoring of crop health and field conditions to improve productivity and reduce crop loss.Traditional plant disease identification mainly depends on manual observation by farmers or agricultural experts, which may be time-consuming, less accurate, and difficult to perform continuously in large fields.In this paper, we propose a smart agriculture system using ESP32, ESP32-CAM, and Convolutional Neural Network (CNN) based leaf disease detection to support real-time crop monitoring.The proposed system monitors environmental parameters such as soil moisture, water level, temperature, pump status, and solenoid valve status through an interactive web dashboard.Leaf images are captured using an ESP32-CAM or uploaded manually to the system, and the trained CNN model classifies the disease category from the input image.The system further provides disease description, preventive steps, and treatment recommendations to assist farmers in taking timely action.The integration of IoT-based environmental monitoring and AI-based disease detection improves the efficiency of crop health analysis and supports smart decisionmaking in agriculture.","农业是一个重要的产业，需要持续监测作物健康和田间状况，以提高生产力并减少作物损失。传统的植物病害识别主要依赖农民或农业专家的肉眼观察，这种方式可能耗时、准确性较低，并且难以在大面积农田中持续进行。本文提出了一种智能农业系统，利用ESP32、ESP32-CAM和基于卷积神经网络（CNN）的叶片病害检测来支持实时作物监测。所提出的系统通过交互式网页仪表盘监测土壤湿度、水位、温度、水泵状态和电磁阀状态等环境参数。叶片图像通过ESP32-CAM采集或手动上传至系统，训练好的CNN模型对输入图像进行病害类别分类。该系统进一步提供病害描述、预防步骤和治疗建议，以帮助农民及时采取行动。基于物联网的环境监测与基于人工智能的病害检测相结合，提高了作物健康分析的效率，并支持农业中的智能决策。","2026-10-05T00:00:00Z",47,{"impact":27,"substance":498,"depth":141,"authority":287,"freshness":27,"relevant":8,"comment":1707},"ESP32+CNN 的作物病害检测系统方案，技术路线常规、期刊权威性弱，属细分技术进展，未达每日精选门槛。",[1709],{"name":18,"url":1701},[32,33,34,82,1711],"病害识别",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":1715},{"candidates":1716,"agricultural_evidence":51},[1717,1721,1725,1729],{"code":78,"score":141,"supported":51,"title":1718,"abstract":1719,"tags":1720},[461],[461],[32,82],{"code":62,"score":79,"supported":51,"title":1722,"abstract":1723,"tags":1724},[],[34,4],[34],{"code":197,"score":87,"supported":51,"title":1726,"abstract":1727,"tags":1728},[],[200,201],[],{"code":50,"score":406,"supported":22,"title":1730,"abstract":1731,"tags":1732},[],[],[33,1711],[1734,1735],"ESP32 智慧农业","ESP32-CAM 叶片病害检测","ESP32智慧农业-4298","10.25258\u002Fijddt.16.48s.39",{"doi":1737,"openalex_id":1739,"authors":1740,"venue":18,"cited_by_count":119,"oa_url":1701,"card":1753,"direction":128,"ingested_from":130},"W7220453212",[1741,1743,1745,1747,1749,1751],{"name":1742,"orcid":17},"R. Jayalakshmi",{"name":1744,"orcid":17},"R. G. Suresh Kumar",{"name":1746,"orcid":17},"Bharath P",{"name":1748,"orcid":17},"Harishvarsakthi A",{"name":1750,"orcid":17},"Abhimanyu Dinesan",{"name":1752,"orcid":17},"Mohammed Fayid Faizal",{"tldr":1754,"method":1755,"finding":1756,"direction":128,"opportunity":1757},"提出基于ESP32与CNN的智能农业系统，实现环境监测与叶片病害实时检测。","ESP32、ESP32-CAM采集环境与图像数据，CNN分类叶片病害，Web仪表","IoT环境监测与AI病害检测集成可提升作物健康分析效率并支持智能决策。","可研究边缘端轻量CNN部署与多病害实时识别，降低对云端和人工的依赖。","2026-10-06T23:30:08.152961Z",{"id":1760,"title":1761,"url":1762,"summary":1763,"summary_zh":1764,"content":17,"source_name":1765,"source_url":1762,"published_at":1704,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":679,"score_detail":1766,"sources":1768,"tags":1770,"primary_topic":1772,"secondary_topics":1773,"classification":1775,"search_phrases":1794,"slug":1797,"view_count":8,"doi":1798,"paper":1799,"created_at":1811},4296,"Smart Irrigation System for Precision Farming","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.151010","Smart Irrigation System for Precision Farming is anIoT-based and sensor-driven approach designed to improve water management, crop productivity, and agricultural sustainability.Conventional irrigation practices often depend on fixed schedules or manual decisions, which can result in over-irrigation, under-irrigation, water wastage, and increased labour requirements.The proposed system integrates soil-moisture, temperature, humidity, and rainfall sensors with a microcontroller such as Arduino or ESP32 to continuously monitor field conditions in real time.Based on the collected data, the system automatically determines the irrigation requirement and controls water pumps or solenoid valves accordingly.Weather information can also be incorporated to avoid unnecessary irrigation during expected rainfall and to adapt watering according to environmental conditions.IoT connectivity enables farmers to remotely monitor soil and weather parameters through a web or mobile dashboard.The system provides a low-cost, scalable, and farmer-friendly solution that supports precision agriculture by delivering the right amount of water at the right time.Such intelligent irrigation can conserve water and energy, reduce manual effort, improve crop health, and contribute to sustainable agricultural development. IJARCCE","基于物联网和传感器的精准农业智能灌溉系统，旨在改善水资源管理、作物生产力和农业可持续性。传统灌溉方式往往依赖固定时间表或人工决策，可能导致过度灌溉、灌溉不足、水资源浪费以及劳动力需求增加。所提出的系统将土壤湿度、温度、湿度和降雨传感器与Arduino或ESP32等微控制器集成，以实时持续监测田间状况。基于采集的数据，系统自动判断灌溉需求，并相应控制水泵或电磁阀。系统还可纳入天气信息，以避免在预计降雨期间进行不必要的灌溉，并根据环境条件调整浇水。物联网连接使农民能够通过网络或移动仪表板远程监测土壤和天气参数。该系统提供了一种低成本、可扩展且对农民友好的解决方案，通过在恰当的时间提供恰当的水量来支持精准农业。这种智能灌溉能够节约水和能源、减少人工劳动、改善作物健康，并促进农业可持续发展。IJARCCE","IJARCCE",{"impact":27,"substance":498,"depth":141,"authority":87,"freshness":27,"relevant":8,"comment":1767},"面向精准农业的物联网智能灌溉系统论文，方法常规、缺乏大规模实测数据，属细分领域技术进展，公共价值有限。",[1769],{"name":1765,"url":1762},[32,34,503,82,1771],"传感器",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[1774],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1776},{"candidates":1777,"agricultural_evidence":51},[1778,1782,1786,1790],{"code":158,"score":24,"supported":51,"title":1779,"abstract":1780,"tags":1781},[159],[186,187,159],[503],{"code":78,"score":27,"supported":51,"title":1783,"abstract":1784,"tags":1785},[],[82,83],[32,82],{"code":62,"score":27,"supported":51,"title":1787,"abstract":1788,"tags":1789},[],[34,4],[1771,34],{"code":197,"score":87,"supported":51,"title":1791,"abstract":1792,"tags":1793},[],[200,201],[],[1795,1796],"智能灌溉系统 精准农业","物联网 土壤湿度传感器 灌溉","智能灌溉系统精准农业-4296","10.17148\u002Fijarcce.2026.151010",{"doi":1798,"openalex_id":1800,"authors":1801,"venue":1765,"cited_by_count":119,"oa_url":1762,"card":1806,"direction":128,"ingested_from":130},"W7220437851",[1802,1804],{"name":1803,"orcid":17},"Mr. Bhagyesh N Chaudhari",{"name":1805,"orcid":17},"Dr. Dinesh D Puri",{"tldr":1807,"method":1808,"finding":1809,"direction":128,"opportunity":1810},"基于物联网与传感器构建智能灌溉系统，实时监测土壤与气象并自动控制灌溉。","用Arduino\u002FESP32连接土壤湿度、温湿度、降雨传感器及物联网仪表盘。","系统可按需精准供水，节水节能、减少人工并提升作物健康与可持续性。","可延伸至多源数据融合的灌溉决策优化、边缘智能控制及大田规模化验证。","2026-10-06T23:30:07.841226Z",{"id":1813,"title":1814,"url":1815,"summary":1816,"summary_zh":1817,"content":17,"source_name":1818,"source_url":1815,"published_at":1704,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":818,"score_detail":1819,"sources":1821,"tags":1823,"primary_topic":1825,"secondary_topics":1826,"classification":1828,"search_phrases":1845,"slug":1848,"view_count":119,"doi":1849,"paper":1850,"created_at":1861},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY",{"impact":27,"substance":174,"depth":498,"authority":87,"freshness":27,"relevant":8,"comment":1820},"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[1822],{"name":1818,"url":1815},[764,32,34,1582,1824],"农业传感器",{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},[1827],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":1829},{"candidates":1830,"agricultural_evidence":51},[1831,1835,1841],{"code":197,"score":498,"supported":51,"title":1832,"abstract":1833,"tags":1834},[201],[200,201],[],{"code":62,"score":835,"supported":51,"title":1836,"abstract":1837,"tags":1840},[],[34,1838,65,4,1839],"无线传感","wireless sensor",[1824,34],{"code":78,"score":27,"supported":51,"title":1842,"abstract":1843,"tags":1844},[],[82,83],[764,32],[1846,1847],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":1849,"openalex_id":1851,"authors":1852,"venue":1818,"cited_by_count":119,"oa_url":1855,"card":1856,"direction":128,"ingested_from":130},"W7220426775",[1853],{"name":1854,"orcid":17},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":1857,"method":1858,"finding":1859,"direction":128,"opportunity":1860},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":1863,"title":1864,"url":1865,"summary":1866,"summary_zh":1867,"content":17,"source_name":1868,"source_url":1865,"published_at":1869,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":496,"score_detail":1870,"sources":1872,"tags":1874,"primary_topic":1877,"secondary_topics":1878,"classification":1879,"search_phrases":1910,"slug":1913,"view_count":119,"doi":17,"paper":1914,"created_at":1924},4217,"An Intelligent IoT-Based Framework for Plant Disease Detection and Crop Advisory Using CNN and ESP32","https:\u002F\u002Fpropulsiontechjournal.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F11261","The increasing demand for sustainable agricultural production has created a need for intelligent systems that continuously monitor crop-growing conditions and support timely agricultural decision-making. Conventional farming practices largely depend on manual observation for monitoring environmental conditions and identifying plant diseases, which can be time-consuming and require expert knowledge. This paper presents an intelligent IoT-based plant disease monitoring and crop advisory system integrating an ESP32 microcontroller, environmental sensors, a Convolutional Neural Network (CNN), and a Flask-based web application. The system continuously monitors temperature, humidity, soil moisture, soil pH, water level, light intensity, and air quality. Sensor readings collected through the ESP32 are processed, stored, and displayed on a real-time web dashboard. Users can also upload plant leaf images, which are preprocessed and analyzed using a trained CNN model to identify disease categories based on visual characteristics such as colour, texture, and disease patterns. The system provides disease information and preventive recommendations. Furthermore, the crop advisory module compares real-time sensor values with predefined crop requirements and generates recommendations for irrigation, soil moisture, pH management, and crop maintenance. The integrated framework reduces dependence on manual inspection and supports data-driven agricultural decisions, providing a practical, scalable, and cost-effective approach to smart agriculture The increasing demand for sustainable agricultural production has created a need for intelligent systems that continuously monitor crop-growing conditions and support timely agricultural decision-making. Conventional farming practices largely depend on manual observation for monitoring environmental conditions and identifying plant diseases, which can be time-consuming and require expert knowledge. This paper presents an intelligent IoT-based plant disease monitoring and crop advisory system integrating an ESP32 microcontroller, environmental sensors, a Convolutional Neural Network (CNN), and a Flask-based web application. The system continuously monitors temperature, humidity, soil moisture, soil pH, water level, light intensity, and air quality. Sensor readings collected through the ESP32 are processed, stored, and displayed on a real-time web dashboard. Users can also upload plant leaf images, which are preprocessed and analyzed using a trained CNN model to identify disease categories based on visual characteristics such as colour, texture, and disease patterns. The system provides disease information and preventive recommendations. Furthermore, the crop advisory module compares real-time sensor values with predefined crop requirements and generates recommendations for irrigation, soil moisture, pH management, and crop maintenance. The integrated framework reduces dependence on manual inspection and supports data-driven agricultural decisions, providing a practical, scalable, and cost-effective approach to smart agriculture","对可持续农业生产的日益增长的需求，催生了对能够持续监测作物生长条件并支持及时农业决策的智能系统的需求。传统农业实践在很大程度上依赖人工观察来监测环境条件和识别植物病害，这可能耗时且需要专业知识。本文提出了一种基于物联网的智能植物病害监测与作物咨询系统，集成了ESP32微控制器、环境传感器、卷积神经网络（CNN）以及基于Flask的Web应用程序。该系统持续监测温度、湿度、土壤水分、土壤pH值、水位、光照强度和空气质量。通过ESP32采集的传感器读数经过处理、存储并显示在实时Web仪表板上。用户还可以上传植物叶片图像，这些图像经过预处理并使用训练好的CNN模型进行分析，以根据颜色、纹理和病害模式等视觉特征识别病害类别。系统提供病害信息和预防建议。此外，作物咨询模块将实时传感器值与预定义的作物需求进行比较，并生成灌溉、土壤水分、pH管理和作物维护方面的建议。该集成框架减少了对人工检查的依赖，支持数据驱动的农业决策，为智慧农业提供了一种实用、可扩展且具有成本效益的方法。对可持续农业生产的日益增长的需求，催生了对能够持续监测作物生长条件并支持及时农业决策的智能系统的需求。传统农业实践在很大程度上依赖人工观察来监测环境条件和识别植物病害，这可能耗时且需要专业知识。本文提出了一种基于物联网的智能植物病害监测与作物咨询系统，集成了ESP32微控制器、环境传感器、卷积神经网络（CNN）以及基于Flask的Web应用程序。该系统持续监测温度、湿度、土壤水分、土壤pH值、水位、光照强度和空气质量。通过ESP32采集的传感器读数经过处理、存储并显示在实时Web仪表板上。用户还可以上传植物叶片图像，这些图像经过预处理并使用训练好的CNN模型进行分析，以根据颜色、纹理和病害模式等视觉特征识别病害类别。系统提供病害信息和预防建议。此外，作物咨询模块将实时传感器值与预定义的作物需求进行比较，并生成灌溉、土壤水分、pH管理和作物维护方面的建议。该集成框架减少了对人工检查的依赖，支持数据驱动的农业决策，为智慧农业提供了一种实用、可扩展且具有成本效益的方法。","Journal of Propulsion Technology","2026-10-03T00:00:00Z",{"impact":27,"substance":174,"depth":498,"authority":87,"freshness":142,"relevant":8,"comment":1871},"将ESP32物联网传感与CNN叶病识别、作物农事建议整合为低成本可扩展框架，方法组合有新意但属常规应用集成，期刊权威性一般，可作为智慧农业技术案例收录。",[1873],{"name":1868,"url":1865},[32,33,34,1875,1876],"农情监测","作物病害识别",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":1880},{"candidates":1881,"agricultural_evidence":51},[1882,1886,1890,1894,1898,1902,1906],{"code":62,"score":397,"supported":51,"title":1883,"abstract":1884,"tags":1885},[4],[34,4],[34],{"code":78,"score":79,"supported":51,"title":1887,"abstract":1888,"tags":1889},[],[32,461],[32],{"code":197,"score":87,"supported":51,"title":1891,"abstract":1892,"tags":1893},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":1895,"abstract":1896,"tags":1897},[],[186,159],[],{"code":926,"score":205,"supported":51,"title":1899,"abstract":1900,"tags":1901},[],[929],[],{"code":50,"score":406,"supported":22,"title":1903,"abstract":1904,"tags":1905},[],[],[1876,33],{"code":633,"score":8,"supported":22,"title":1907,"abstract":1908,"tags":1909},[],[],[1875],[1911,1912],"ESP32 植物病害检测","CNN 作物病害识别","ESP32植物病害检测-4217",{"doi":17,"openalex_id":1915,"authors":1916,"venue":1868,"cited_by_count":119,"oa_url":1865,"card":1919,"direction":128,"ingested_from":130},"W7220154248",[1917],{"name":1918,"orcid":17},"A. Rajani",{"tldr":1920,"method":1921,"finding":1922,"direction":128,"opportunity":1923},"构建ESP32物联网与CNN结合的植物病害检测及作物咨询系统。","ESP32、环境传感器、CNN、Flask网页应用。","系统能实时监测环境、识别病害并给出灌溉与pH等建议。","可探索多模态融合与边缘部署，提升病害早期预警和实时决策能力。","2026-10-05T23:30:10.507455Z",{"id":1926,"title":1927,"url":1928,"summary":1929,"summary_zh":1930,"content":17,"source_name":1931,"source_url":1928,"published_at":1932,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1933,"score_detail":1934,"sources":1936,"tags":1938,"primary_topic":1940,"secondary_topics":1941,"classification":1947,"search_phrases":1983,"slug":1986,"view_count":406,"doi":1987,"paper":1988,"created_at":2001},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",81,{"impact":24,"substance":25,"depth":24,"authority":498,"freshness":142,"relevant":8,"comment":1935},"提出农业数字孪生与GRU融合的实时产量预测与决策支持框架，方法新颖、指标扎实，对智慧农业研究有参考价值。",[1937],{"name":1931,"url":1928},[32,33,34,345,1939],"作物产量预测",{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},[1942,1943],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":190,"slug":1944,"name":1945,"path":1946,"parent_code":162},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":164,"locked":22,"version":45,"method":46,"evidence":1948},{"candidates":1949,"agricultural_evidence":51},[1950,1954,1958,1962,1967,1971,1975,1979],{"code":78,"score":1150,"supported":51,"title":1951,"abstract":1952,"tags":1953},[1152],[32,345,461,1152],[345,32],{"code":50,"score":251,"supported":51,"title":1955,"abstract":1956,"tags":1957},[340],[58],[33],{"code":62,"score":21,"supported":51,"title":1959,"abstract":1960,"tags":1961},[],[34,65,4],[34],{"code":190,"score":142,"supported":51,"title":1963,"abstract":1964,"tags":1966},[],[193,1965,194],"气候适应",[],{"code":197,"score":87,"supported":51,"title":1968,"abstract":1969,"tags":1970},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":1972,"abstract":1973,"tags":1974},[],[186,159],[],{"code":926,"score":205,"supported":51,"title":1976,"abstract":1977,"tags":1978},[],[929],[],{"code":633,"score":8,"supported":22,"title":1980,"abstract":1981,"tags":1982},[],[],[1939],[1984,1985],"Agri Digital Twin GRU 产量预测","物联网 数字孪生 智慧农业","AgriDigitalTwinGRU产量预测-4213","10.1016\u002Fj.engappai.2026.116418",{"doi":1987,"openalex_id":1989,"authors":1990,"venue":1931,"cited_by_count":119,"oa_url":1928,"card":1996,"direction":128,"ingested_from":130},"W7219849006",[1991,1994],{"name":1992,"orcid":1993},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":1995,"orcid":17},"Munish Bhatia",{"tldr":1997,"method":1998,"finding":1999,"direction":128,"opportunity":2000},"提出农业数字孪生框架，结合GRU与物联网实现实时产量预测与可持续决策支持。","基于物联网传感实时采集土壤气象数据，用GRU模型做时序产量预测与分类。","预测MAE 0.0243、R² 0.9254，平均准确率97.41%，优于BiLSTM、LSTM和1","可探索多源遥感与数字孪生融合、跨区域迁移学习及决策闭环验证等空白。","2026-10-05T23:30:09.119891Z",{"id":2003,"title":2004,"url":2005,"summary":2006,"summary_zh":2007,"content":17,"source_name":2008,"source_url":2005,"published_at":2009,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2010,"score_detail":2011,"sources":2013,"tags":2015,"primary_topic":2016,"secondary_topics":2017,"classification":2020,"search_phrases":2040,"slug":2043,"view_count":406,"doi":2044,"paper":2045,"created_at":2056},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":24,"substance":252,"depth":24,"authority":141,"freshness":142,"relevant":8,"comment":2012},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[2014],{"name":2008,"url":2005},[32,33,34,345,82],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[2018,2019],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"code":190,"slug":1944,"name":1945,"path":1946,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":2021},{"candidates":2022,"agricultural_evidence":51},[2023,2027,2031,2035],{"code":62,"score":24,"supported":51,"title":2024,"abstract":2025,"tags":2026},[65],[34,181,65,4,66],[34],{"code":78,"score":251,"supported":51,"title":2028,"abstract":2029,"tags":2030},[],[82,345,83],[345,32,82],{"code":50,"score":251,"supported":51,"title":2032,"abstract":2033,"tags":2034},[58],[58],[33],{"code":190,"score":142,"supported":51,"title":2036,"abstract":2037,"tags":2039},[],[193,194,2038],"climate resilience",[],[2041,2042],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":2044,"openalex_id":2046,"authors":2047,"venue":2008,"cited_by_count":119,"oa_url":2005,"card":2051,"direction":128,"ingested_from":130},"W7216004668",[2048],{"name":2049,"orcid":2050},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":2052,"method":2053,"finding":2054,"direction":997,"opportunity":2055},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z",{"id":2058,"title":2059,"url":2060,"summary":2061,"summary_zh":2062,"content":17,"source_name":2063,"source_url":2060,"published_at":2009,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2064,"score_detail":2065,"sources":2067,"tags":2069,"primary_topic":2071,"secondary_topics":2072,"classification":2073,"search_phrases":2096,"slug":2099,"view_count":406,"doi":2100,"paper":2101,"created_at":2115},4007,"Development of an Iot-Based Automated Plant Monitoring and Watering System Using Two-Input Sugeno Fuzzy Logic","https:\u002F\u002Fdoi.org\u002F10.59688\u002Frdmmvz13","Manual plant irrigation often leads to inefficient water usage and delayed responses to changing environmental conditions. This study aims to develop and implement an Internet of Things (IoT)-based monitoring and automatic irrigation system using ESP32 and ESP32-CAM. The proposed system continuously monitors air temperature, humidity, soil moisture, and light intensity while simultaneously capturing plant images for remote observation through a web-based interface. A zero-order Sugeno Fuzzy Logic algorithm is employed to determine irrigation duration based on soil moisture and light intensity inputs, enabling adaptive watering decisions according to environmental conditions. Sensor data and captured images are transmitted to a remote server and displayed through a monitoring website for real-time access. Outdoor testing demonstrated that the system was able to continuously monitor environmental parameters, perform adaptive irrigation based on fuzzy inference results, successfully transmit sensor data to the database, and synchronize captured images with the web platform. The experimental results indicate that the proposed system can provide reliable monitoring and automated irrigation functions, supporting the implementation of IoT-based smart agriculture applications.","人工灌溉往往导致用水效率低下，并且对不断变化的环境条件响应滞后。本研究旨在开发并实现一个基于物联网（IoT）的监测与自动灌溉系统，采用ESP32和ESP32-CAM。所提出的系统持续监测空气温度、湿度、土壤湿度和光照强度，同时采集植物图像，通过基于网页的界面进行远程观察。系统采用零阶Sugeno模糊逻辑算法，根据土壤湿度和光照强度输入确定灌溉时长，从而依据环境条件实现自适应浇水决策。传感器数据和采集的图像被传输至远程服务器，并通过监测网站显示，以供实时访问。户外测试表明，该系统能够持续监测环境参数，基于模糊推理结果执行自适应灌溉，成功将传感器数据传输至数据库，并使采集的图像与网络平台同步。实验结果表明，所提出的系统能够提供可靠的监测和自动灌溉功能，支持基于物联网的智慧农业应用的实施。","BULLETIN OF NETWORK ENGINEER AND INFORMATICS",61,{"impact":27,"substance":24,"depth":397,"authority":21,"freshness":21,"relevant":8,"comment":2066},"基于ESP32与Sugeno模糊逻辑的IoT自动灌溉系统研究，方法具体、实验完整，对智慧农业应用有参考价值，但属细分技术论文，公共影响有限。",[2068],{"name":2063,"url":2060},[32,33,34,503,2070],"环境监测",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2074},{"candidates":2075,"agricultural_evidence":51},[2076,2080,2084,2088,2092],{"code":62,"score":24,"supported":51,"title":2077,"abstract":2078,"tags":2079},[4],[34,65,4],[34],{"code":158,"score":79,"supported":51,"title":2081,"abstract":2082,"tags":2083},[],[186,159],[503],{"code":78,"score":79,"supported":51,"title":2085,"abstract":2086,"tags":2087},[],[32,461],[32],{"code":197,"score":87,"supported":51,"title":2089,"abstract":2090,"tags":2091},[],[200,201],[],{"code":50,"score":8,"supported":22,"title":2093,"abstract":2094,"tags":2095},[],[],[33],[2097,2098],"ESP32 自动灌溉 系统","Sugeno 模糊逻辑 灌溉","ESP32自动灌溉系统-4007","10.59688\u002Frdmmvz13",{"doi":2100,"openalex_id":2102,"authors":2103,"venue":2063,"cited_by_count":119,"oa_url":2060,"card":2110,"direction":128,"ingested_from":130},"W7215131860",[2104,2106,2108],{"name":2105,"orcid":17},"Muhammad Viki Agung Prakoso",{"name":2107,"orcid":17},"Ahmad Taqwa",{"name":2109,"orcid":17},"Nasron",{"tldr":2111,"method":2112,"finding":2113,"direction":128,"opportunity":2114},"开发基于ESP32与ESP32-CAM的物联网植物监测与自动灌溉系统，用Sugeno模糊逻辑决策浇水","ESP32\u002FESP32-CAM采集温湿度、土壤水分、光照与图像，零阶Sugeno","系统可连续监测环境参数、自适应灌溉，并成功将数据与图像同步至网页平台。","可引入作物生长模型或机器学习优化模糊规则，并结合作物图像实现缺水胁迫的视觉诊断。","2026-10-02T23:30:11.514000Z",{"id":2117,"title":2118,"url":2119,"summary":2120,"summary_zh":2121,"content":17,"source_name":2122,"source_url":2119,"published_at":2123,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2124,"score_detail":2125,"sources":2127,"tags":2129,"primary_topic":2131,"secondary_topics":2132,"classification":2134,"search_phrases":2149,"slug":2152,"view_count":8,"doi":2153,"paper":2154,"created_at":2169},3885,"InResKolArno_SCO: An Efficient Plant Disease Classification Framework for IoT-Enabled Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.1142\u002Fs0218001426500503","This study proposes a hybrid approach for plant disease classification, integrating Swarm Cray Optimization (SCO) with the InResKolArno, a deep learning model, to address challenges like environmental sensitivity, high computational demands, and limited resources in IoT environments. The process begins with collecting a plant disease recognition dataset, followed by image preprocessing steps such as nearest neighbor interpolation and normalization to improve quality. Disease regions are isolated using color-based thresholding for segmentation. Feature extraction is carried out using deep convolutional neural networks (DCNN), specifically VGG16 and GoogLeNet, to extract high-level leaf features. The InResKolArno classifier, enhanced by SCO (a combination of Particle Swarm Optimization and Crayfish Optimization), is employed to categorize the leaves into three classes: healthy, rust, and powdery mildew. This method improves classification accuracy and reduces data transmission costs in IoT-based precision farming systems. The proposed model achieves high performance with an accuracy of 99.41% and a Positive Predictive Value (PPV) of 99.69%, identifying plant diseases in just 28 seconds. This hybrid model provides superior computational efficiency, making it suitable for real-time deployment in smart farming applications, effectively identifying plant diseases for early intervention and better crop management.","本研究提出了一种植物病害分类的混合方法，将群虾优化算法（Swarm Cray Optimization，SCO）与深度学习模型InResKolArno相结合，以应对物联网环境中的环境敏感性、高计算需求和资源受限等挑战。该流程首先收集植物病害识别数据集，随后进行图像预处理步骤，如最近邻插值和归一化，以提高图像质量。利用基于颜色的阈值分割方法分离病害区域。采用深度卷积神经网络（DCNN），具体为VGG16和GoogLeNet，进行特征提取，以获取叶片的高层特征。经SCO（粒子群优化与螯虾优化的结合）增强的InResKolArno分类器用于将叶片分为三类：健康、锈病和白粉病。该方法提高了分类准确率，并降低了基于物联网的精准农业系统中的数据传输成本。所提出的模型实现了高性能，准确率达99.41%，阳性预测值（PPV）达99.69%，仅需28秒即可识别植物病害。该混合模型具有优越的计算效率，适用于智能农业应用中的实时部署，能够有效识别植物病害，从而实现早期干预和更好的作物管理。","International Journal of Pattern Recognition and Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":174,"substance":616,"depth":26,"authority":141,"freshness":142,"relevant":8,"comment":2126},"提出SCO优化InResKolArno的植物病害分类框架，准确率99.41%、28秒识别，面向IoT智慧农业实时部署，方法新颖且数据可靠，值得入选。",[2128],{"name":2122,"url":2119},[32,33,34,82,2130],"植物病害识别",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[2133],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":2135},{"candidates":2136,"agricultural_evidence":51},[2137,2141,2145],{"code":62,"score":397,"supported":51,"title":2138,"abstract":2139,"tags":2140},[4],[34,4],[34],{"code":78,"score":141,"supported":51,"title":2142,"abstract":2143,"tags":2144},[461],[82],[32,82],{"code":50,"score":27,"supported":51,"title":2146,"abstract":2147,"tags":2148},[],[55,53],[33,2130],[2150,2151],"InResKolArno SCO 植物病害分类","VGG16 GoogLeNet 病害识别","InResKolArnoSCO植物病害分类-3885","10.1142\u002Fs0218001426500503",{"doi":2153,"openalex_id":2155,"authors":2156,"venue":2122,"cited_by_count":119,"oa_url":17,"card":2164,"direction":128,"ingested_from":130},"W7214976120",[2157,2160,2162],{"name":2158,"orcid":2159},"Chitoor Venkat Rao Ajay Kumar","https:\u002F\u002Forcid.org\u002F0009-0001-4082-5416",{"name":2161,"orcid":17},"C. Anbuananth",{"name":2163,"orcid":17},"Pandi Chiranjeevi",{"tldr":2165,"method":2166,"finding":2167,"direction":128,"opportunity":2168},"提出InResKolArno_SCO混合框架，用于物联网智慧农业中的植物病害分类。","VGG16与GoogLeNet提取特征，结合粒子群与螯虾优化算法优化分类器。","在健康、锈病、白粉病三分类上准确率达99.41%，仅需28秒。","可探索轻量化模型在边缘设备上的部署，并扩展至更多病害与田间复杂环境验证。","2026-10-01T23:30:09.723646Z",{"id":2171,"title":2172,"url":2173,"summary":2174,"summary_zh":2175,"content":17,"source_name":2176,"source_url":2173,"published_at":2123,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1517,"score_detail":2177,"sources":2179,"tags":2181,"primary_topic":2184,"secondary_topics":2185,"classification":2186,"search_phrases":2206,"slug":2209,"view_count":406,"doi":2210,"paper":2211,"created_at":2231},3881,"A Human-Centered Machine Learning Dashboard for Real-Time Smart Farming Decision Support","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fshem.v2i2.302","The integration of machine learning (ML) into precision agriculture has advanced predictive accuracy for tasks such as crop monitoring, irrigation scheduling, and yield estimation. However, many existing IoT-based smart farming systems remain technically driven, offering limited attention to how farmers and field operators actually interact with and interpret model outputs. This gap often results in low adoption rates, as complex predictions fail to translate into actionable, trustworthy decisions for end users. This study proposes a human-centered machine learning dashboard designed to bridge predictive modeling with real-time, farmer-friendly decision support. The system integrates IoT sensor data streams with ML-based prediction modules, presented through an interactive, usability-tested interface that emphasizes clarity, accessibility, and interpretability over raw technical complexity. A working prototype was developed and evaluated using both quantitative usability metrics (e.g., task completion time, System Usability Scale scores) and qualitative feedback from target users to assess the dashboard's effectiveness in supporting field-level decisions. The proposed system architecture, dashboard interface, and underlying ML pipeline are implemented as open and reproducible source code, enabling replication and adaptation across similar smart farming contexts. Results indicate that the human-centered design significantly improves users' comprehension of model outputs and their confidence in acting on system recommendations, without compromising predictive performance. This research contributes a practical framework for designing machine intelligence systems that prioritize end-user experience alongside technical accuracy, offering implications for the broader development of human-centered AI in agricultural and IoT-driven decision support environments.","机器学习（ML）在精准农业中的融合提升了作物监测、灌溉调度和产量估算等任务的预测准确性。然而，许多现有的基于物联网的智慧农业系统仍然以技术驱动为主，对农民和田间操作人员实际如何与模型输出交互并加以解读关注有限。这一差距往往导致采用率偏低，因为复杂的预测无法转化为最终用户可操作、可信赖的决策。本研究提出了一种以人为中心的机器学习仪表盘，旨在将预测建模与实时、面向农民的决策支持相衔接。该系统将物联网传感器数据流与基于机器学习的预测模块相集成，并通过一个经过可用性测试的交互式界面呈现，强调清晰性、可及性和可解释性，而非原始的技术复杂性。我们开发了一个可运行的原型，并使用定量可用性指标（如任务完成时间、系统可用性量表评分）和来自目标用户的定性反馈进行评估，以考察该仪表盘在支持田间决策方面的有效性。所提出的系统架构、仪表盘界面和底层机器学习流水线均以开放且可复现的源代码形式实现，便于在类似的智慧农业场景中复制和适配。结果表明，以人为中心的设计显著提升了用户对模型输出的理解以及依据系统建议采取行动的信心，同时未损害预测性能。本研究为设计在技术准确性的同时优先考虑最终用户体验的机器智能系统提供了一个实用框架，对农业及物联网驱动的决策支持环境中以人为中心的人工智能的更广泛发展具有启示意义。","Smart Human-centered Emerging Research in Machine Intelligence",{"impact":251,"substance":24,"depth":174,"authority":21,"freshness":27,"relevant":8,"comment":2178},"提出以人为本的机器学习仪表盘，兼顾可用性与预测性能，并开源实现，对智慧农业落地有参考价值。",[2180],{"name":2176,"url":2173},[32,33,34,2182,2183],"决策支持","人机交互",{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2187},{"candidates":2188,"agricultural_evidence":51},[2189,2194,2198,2202],{"code":50,"score":2190,"supported":51,"title":2191,"abstract":2192,"tags":2193},27,[171,340],[170,171,340],[33,2182],{"code":78,"score":21,"supported":51,"title":2195,"abstract":2196,"tags":2197},[],[32,82,83],[32],{"code":62,"score":79,"supported":51,"title":2199,"abstract":2200,"tags":2201},[],[34,4],[34],{"code":158,"score":87,"supported":51,"title":2203,"abstract":2204,"tags":2205},[],[186,159],[],[2207,2208],"农业人工智能 人机交互 决策支持 智慧农业","农业人工智能 人机交互","农业人工智能人机交互决策支持智慧农业-3881","10.68012\u002Fshem.v2i2.302",{"doi":2210,"openalex_id":2212,"authors":2213,"venue":2176,"cited_by_count":119,"oa_url":2173,"card":2226,"direction":128,"ingested_from":130},"W7214922384",[2214,2217,2219,2221,2223],{"name":2215,"orcid":2216},"Adam Faturahman","https:\u002F\u002Forcid.org\u002F0000-0001-9727-9092",{"name":2218,"orcid":17},"James Williams",{"name":2220,"orcid":17},"Evelyn Anderson",{"name":2222,"orcid":17},"Goh Yao",{"name":2224,"orcid":2225},"Muhammad Rifat Syauqy","https:\u002F\u002Forcid.org\u002F0009-0001-4582-5693",{"tldr":2227,"method":2228,"finding":2229,"direction":128,"opportunity":2230},"提出以人为中心的机器学习仪表盘，融合物联网与预测模型，为农户提供实时可解释的决策支持。","集成物联网传感器数据流与机器学习预测模块，开发交互式仪表盘，并用SUS量表和用户","以人为中心的设计显著提升用户对模型输出的理解与采纳信心，且不牺牲预测性能。","可探索不同作物、地区和文化背景下农户对可解释AI界面的差异化需求与长期采纳效果。","2026-10-01T23:30:09.481397Z",{"id":2233,"title":2234,"url":2235,"summary":2236,"summary_zh":2237,"content":17,"source_name":2238,"source_url":2235,"published_at":2239,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1459,"score_detail":2240,"sources":2242,"tags":2244,"primary_topic":2245,"secondary_topics":2246,"classification":2248,"search_phrases":2267,"slug":2270,"view_count":1203,"doi":2271,"paper":2272,"created_at":2280},3771,"IOT-BASED SOIL CONDITION ASSESSMENT IN SMART AGRICULTURE: A REVIEW OF INDICATORS, COMPOSITE INDICES AND DECISION-SUPPORT SYSTEMS","https:\u002F\u002Fdoi.org\u002F10.17683\u002Fijomam\u002Fissue25\u002Fv1.24","Soil condition assessment is increasingly supported by mechatronic sensing and Internet of Things (IoT) technologies that complement laboratory and field evaluation with continuous and distributed measurements.This review focuses on the complete information chain required for IoT-based soil assessment: indicator selection, sensing and communication, data-quality control, normalization, composite index construction and decision support.Physical, chemical and biological indicators are considered together, while their suitability for continuous monitoring is differentiated.Soil moisture, temperature and electrical conductivity are readily integrated into IoT platforms; pH and nutrient sensing require more rigorous calibration, whereas organic matter, structural and biological indicators often remain periodic laboratory or field inputs.A compact processing framework is proposed in which sensor records are validated, filtered and normalized before aggregation into physical, chemical and biological sub-indices and a conceptual Soil Condition Index (SCI).The SCI is then linked to transparent rule-based alerts and a modular end-toend architecture connecting sensing nodes, communication, database services and user interfaces.The proposed framework is intended as an interpretation and early-warning layer rather than an autonomous agronomic decision-making system.Its practical application requires local calibration, independent validation and explicit reporting of data completeness.An experimental photovoltaicpowered IoT prototype is also presented to illustrate the practical implementation of the multidepth sensing, acquisition and 4G communication layers.The review highlights persistent gaps in multi-parameter integration, sensor-data quality control, local calibration and the transformation of raw measurements into transparent decision-support outputs.","土壤状况评估正日益得到机电传感与物联网（IoT）技术的支持，这些技术通过连续和分布式测量补充了实验室与田间评价。本综述聚焦于基于物联网的土壤评估所需的完整信息链：指标选择、传感与通信、数据质量控制、归一化、综合指数构建及决策支持。物理、化学和生物指标被一并考虑，同时对其适用于连续监测的程度加以区分。土壤水分、温度和电导率易于集成到物联网平台中；pH和养分传感需要更严格的校准，而有机质、结构及生物指标通常仍依赖周期性的实验室或田间输入。本文提出一个紧凑的处理框架，其中传感器记录在聚合为物理、化学和生物子指数以及一个概念性土壤状况指数（SCI）之前，先经过验证、滤波和归一化。随后，SCI与透明的基于规则的警报以及一个连接传感节点、通信、数据库服务和用户界面的模块化端到端架构相链接。所提出的框架旨在作为解释与预警层，而非自主的农艺决策系统。其实际应用需要本地校准、独立验证以及数据完整性的明确报告。本文还展示了一个实验性光伏供电物联网原型，以说明多深度传感、采集和4G通信层的实际实现。本综述强调了在多参数集成、传感器数据质量控制、本地校准以及将原始测量转化为透明决策支持输出方面持续存在的差距。","International Journal of Mechatronics and Applied Mechanics","2026-09-28T00:00:00Z",{"impact":251,"substance":252,"depth":26,"authority":251,"freshness":27,"relevant":8,"comment":2241},"系统梳理物联网土壤评估的指标体系与决策支持框架，方法链条完整，但属综述性论文，实践影响有限。",[2243],{"name":2238,"url":2235},[32,34,2182,1771,1582],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[2247],{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":2249},{"candidates":2250,"agricultural_evidence":51},[2251,2255,2259,2263],{"code":62,"score":1150,"supported":51,"title":2252,"abstract":2253,"tags":2254},[4],[34,65,4],[1771,34],{"code":197,"score":498,"supported":51,"title":2256,"abstract":2257,"tags":2258},[201],[200,201],[],{"code":78,"score":142,"supported":51,"title":2260,"abstract":2261,"tags":2262},[461],[],[32],{"code":50,"score":287,"supported":51,"title":2264,"abstract":2265,"tags":2266},[],[340],[2182],[2268,2269],"IoT 土壤评估 智慧农业","土壤状况指数 SCI","IoT土壤评估智慧农业-3771","10.17683\u002Fijomam\u002Fissue25\u002Fv1.24",{"doi":2271,"openalex_id":2273,"authors":2274,"venue":2238,"cited_by_count":119,"oa_url":2235,"card":2275,"direction":128,"ingested_from":130},"W7214635996",[],{"tldr":2276,"method":2277,"finding":2278,"direction":128,"opportunity":2279},"综述物联网土壤评估全信息链，提出土壤状况指数与决策支持框架。","文献综述，整合物理化学生物指标、传感通信、数据质控与复合指数构建。","水分温度电导率易集成，pH养分需校准，有机质生物指标仍依赖实验室。","多参数融合、传感器数据质控、本地校准及透明决策输出仍是研究空白。","2026-09-29T23:30:09.167877Z",{"id":2282,"title":2283,"url":2284,"summary":2285,"summary_zh":17,"content":2286,"source_name":2287,"source_url":17,"published_at":2288,"category":2289,"cover_url":17,"hotness":21,"is_selected":22,"score":2290,"score_detail":2291,"sources":2293,"tags":2295,"primary_topic":2299,"secondary_topics":2304,"classification":2305,"search_phrases":2325,"slug":2328,"view_count":287,"doi":17,"paper":17,"created_at":2329},3705,"相城入选第三批江苏省数字乡村试点","https:\u002F\u002Fwww.suzhou.gov.cn\u002Fszsrmzf\u002Fqxkx\u002F202609\u002F748b6d756e474a5394751df66afeaca1.shtml","相城区入选第三批江苏省数字乡村试点，依托区级数字乡村一件事平台落地美人腿数字乡村驾驶舱，构建水陆空一体化感知体系：无人机全域巡航、水质传感器实时跟踪、摄像头物联网全天候值守。相城区累计建成11个市级智慧农村。","初秋的阳澄湖，水面澄碧，湖畔蟹塘连片。走进度假区（阳澄湖镇）美人腿数字乡村驾驶舱，工作人员轻点鼠标，这片湖区的一举一动，正被一张“数字网”尽收眼底。\n\n近日，在省委网信办、省农业农村厅召开的江苏省数字乡村建设工作现场推进会上，相城区入选第三批江苏省数字乡村试点。\n\n美人腿半岛是阳澄湖大闸蟹的核心养殖区，也是相城农文旅融合发展的“黄金岸线”。相城区依托区级“数字乡村一件事”平台，落地美人腿数字乡村驾驶舱，打通产业、生态、治理之间的数据壁垒。\n\n平台汇聚农业、环保、文旅、网格等多源业务信息，搭建起“水陆空”一体化感知体系：空中无人机开展全域巡航，对农田火情、河道隐患提前预警；水中水质传感器实时跟踪水体指标；岸边摄像头、物联网感知终端全天候值守，针对非法捕捞、防溺水风险自动识别告警。基层治理由此从“事后被动处置”转向“主动感知、提前预警”，湖区实现一网统管、精准施策。\n\n近年来，相城区构建“一套标准+N个通用应用”建设体系，实现全区71个行政村数字化基础应用全覆盖，同时鼓励各村立足资源禀赋，开发“小巧灵”本土化特色场景，探索“千村千面”的差异化建设模式。目前，全区累计建成11个市级“智慧农村”。","苏州市人民政府","2026-09-24T00:00:00Z","报道",65,{"impact":24,"substance":174,"depth":141,"authority":251,"freshness":87,"relevant":8,"comment":2292},"省级数字乡村试点落地报道，含平台架构与治理场景细节，具行业参考价值。",[2294],{"name":2287,"url":2284},[2296,2297,32,34,2298],"数字乡村","乡村治理","江苏苏州",{"code":2300,"slug":2301,"name":2302,"path":2303,"parent_code":38},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2306},{"candidates":2307,"agricultural_evidence":51},[2308,2312,2316,2321],{"code":2300,"score":251,"supported":51,"title":2309,"abstract":2310,"tags":2311},[2296],[2296],[2296],{"code":62,"score":287,"supported":51,"title":2313,"abstract":2314,"tags":2315},[],[34],[34],{"code":2317,"score":8,"supported":22,"title":2318,"abstract":2319,"tags":2320},"G5",[],[],[2297],{"code":78,"score":8,"supported":22,"title":2322,"abstract":2323,"tags":2324},[],[],[32],[2326,2327],"相城区 数字乡村试点","美人腿 数字乡村驾驶舱","相城区数字乡村试点-3705","2026-09-29T00:08:19.875863Z",{"id":2331,"title":2332,"url":2333,"summary":2334,"summary_zh":2335,"content":17,"source_name":2336,"source_url":2333,"published_at":2337,"category":20,"cover_url":17,"hotness":2338,"is_selected":22,"score":249,"score_detail":2339,"sources":2341,"tags":2345,"primary_topic":2346,"secondary_topics":2347,"classification":2348,"search_phrases":2375,"slug":2378,"view_count":406,"doi":2379,"paper":2380,"created_at":2392},3616,"Integrated Machine Learning for Agriculture, Environment and Health-Adjacent Systems: Solar Cell Screening, Reservoir Monitoring and IoT-Driven Advisory Across Interdisciplinary Contexts","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991","This paper explores the convergence of precision agriculture and environmental monitoring by taking advantage of overlapping physical sensor infrastructure. The authors present three interdisciplinary machine learning studies built upon a shared edge-inference architecture optimized for low-latency execution on a Raspberry Pi 4. First, the framework accelerates agrivoltaic deployments by combining density functional theory with gradient boosting regressors to screen perovskite solar cell efficiencies without physical fabrication cycles. Second, it introduces a hybrid 1D-CNN and random forest model to detect water level anomalies in Bangladesh's Kaptai Reservoir, reducing RMSE by 35% over a 40-year validation window. Third, it details an IoT agricultural platform (\"AgriLM\") that pairs ResNet-50 pest detection with an instruction-tuned LLM layer to deliver natural language, climate-adaptive management advice to smallholder farmers, optimizing cross-system resource constraints.","本文探讨了通过利用重叠的物理传感器基础设施，实现精准农业与环境监测的融合。作者提出了三项跨学科机器学习研究，均构建于一个共享的边缘推理架构之上，该架构针对树莓派4（Raspberry Pi 4）上的低延迟执行进行了优化。首先，该框架将密度泛函理论与梯度提升回归器相结合，无需物理制造周期即可筛选钙钛矿太阳能电池效率，从而加速农业光伏部署。其次，该框架引入了一种混合一维卷积神经网络（1D-CNN）与随机森林模型，用于检测孟加拉国卡普泰水库（Kaptai Reservoir）的水位异常，在40年验证窗口内将均方根误差（RMSE）降低了35%。第三，该框架详细介绍了物联网农业平台（“AgriLM”），该平台将ResNet-50害虫检测与指令微调的大语言模型（LLM）层相结合，为小农户提供自然语言、气候适应性的管理建议，并优化跨系统资源约束。","Figshare","2026-09-26T00:00:00Z",25,{"impact":251,"substance":25,"depth":24,"authority":142,"freshness":142,"relevant":8,"comment":2340},"跨学科机器学习论文，方法新颖、数据扎实，但偏学术预印本，产业落地影响有限。",[2342,2343],{"name":2336,"url":2333},{"name":2336,"url":2344},"https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991.v1",[32,33,34,622,1325],{"code":38,"slug":39,"name":40,"path":41,"parent_code":17},[],{"status":44,"locked":22,"version":45,"method":46,"evidence":2349},{"candidates":2350,"agricultural_evidence":51,"reason":114},[2351,2355,2359,2363,2367,2371],{"code":50,"score":397,"supported":51,"title":2352,"abstract":2353,"tags":2354},[171],[170,171],[33],{"code":62,"score":397,"supported":51,"title":2356,"abstract":2357,"tags":2358},[4],[34,4],[34],{"code":78,"score":79,"supported":51,"title":2360,"abstract":2361,"tags":2362},[],[82,83],[32],{"code":1345,"score":87,"supported":51,"title":2364,"abstract":2365,"tags":2366},[],[1348,1349],[],{"code":190,"score":205,"supported":51,"title":2368,"abstract":2369,"tags":2370},[],[1965],[],{"code":633,"score":8,"supported":22,"title":2372,"abstract":2373,"tags":2374},[],[],[622],[2376,2377],"Kaptai Reservoir 水位监测","AgriLM 物联网农业平台","KaptaiReservoir水位监测-3616","10.6084\u002Fm9.figshare.34001991",{"doi":2379,"openalex_id":2381,"authors":2382,"venue":2336,"cited_by_count":119,"oa_url":2333,"card":2387,"direction":128,"ingested_from":130},"W7214485392",[2383,2385],{"name":2384,"orcid":17},"Alepera Rasaed",{"name":2386,"orcid":17},"Minaya Aleguja",{"tldr":2388,"method":2389,"finding":2390,"direction":128,"opportunity":2391},"用共享边缘推理架构整合机器学习，实现太阳能电池筛选、水库监测与IoT农业咨询。","Raspberry Pi 4边缘推理，DFT+梯度提升、1D-CNN+随机森林、","混合模型将水库水位异常检测RMSE降低35%，AgriLM可提供自然语言气候适应建议。","可探索多任务边缘模型在农业-环境-健康跨域场景下的能效与泛化性权衡。","2026-09-27T23:30:24.718444Z",{"id":2394,"title":2395,"url":2396,"summary":2397,"summary_zh":2398,"content":17,"source_name":2399,"source_url":2396,"published_at":2400,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2401,"score_detail":2402,"sources":2404,"tags":2406,"primary_topic":2409,"secondary_topics":2410,"classification":2411,"search_phrases":2426,"slug":2429,"view_count":205,"doi":2430,"paper":2431,"created_at":2445},3540,"Smart Drip Irrigation Maintains Phytosanitary Superiority under Dry Season Conditions in Shaded Cultivation of Celery (Apium graveolens L.) in Guyana","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.af-1081","Celery (Apium graveolens L.) is a commercially important crop in Guyana especially with the advancing of shaded cultivation as part of the national Climate-Smart Agriculture agenda. A companion study evaluating smart drip and sprinkler irrigation during the wet season (April-June 2025) demonstrated that smart drip irrigation significantly reduced foliar disease incidence while maintaining equivalent biomass yields. This study extends the investigation to the dry season (July-October 2025) to determine whether the phytosanitary and yield advantages of smart drip irrigation are sustained. A two-treatment, shade house trial (n = 6 beds per treatment, 504 plants per treatment) was conducted using an automated IoT irrigation system. No statistically significant difference in plant height, leaf length, leaf width or fresh biomass was detected between treatments (p greater than 0.05 for all parameters). Smart drip irrigation yielded a disease incidence of 2.8% compared to 9.3% under sprinkler irrigation (Fisher’s exact test: p = 1.30 x 10-9 and odds ratio = 0.282). These findings confirm that the phytosanitary superiority of smart drip irrigation is sustained across seasons, supporting its utilization for year-round shade house celery production in tropical contexts.","芹菜（Apium graveolens L.）是圭亚那具有重要商业价值的作物，尤其是在遮阴栽培作为国家气候智慧型农业议程的一部分不断推进的背景下。一项评估雨季（2025年4月至6月）智能滴灌与喷灌的配套研究表明，智能滴灌显著降低了叶部病害发生率，同时保持了同等的生物量产量。本研究将调查扩展至旱季（2025年7月至10月），以确定智能滴灌在植物检疫和产量方面的优势是否能够持续。采用自动化物联网灌溉系统开展了双处理遮阴棚试验（每处理6个苗床，n = 6；每处理504株植物）。两种处理间在株高、叶长、叶宽或鲜生物量方面均未检测到统计学显著差异（所有参数p大于0.05）。智能滴灌的病害发生率为2.8%，而喷灌为9.3%（Fisher精确检验：p = 1.30 x 10-9，优势比 = 0.282）。这些发现证实，智能滴灌在植物检疫方面的优越性在不同季节间均能持续，支持其用于热带地区遮阴棚芹菜的全年生产。","Indian Journal of Agricultural Research","2026-09-25T00:00:00Z",71,{"impact":251,"substance":616,"depth":26,"authority":141,"freshness":27,"relevant":8,"comment":2403},"热带遮阴栽培下智能滴灌跨季节保持植保优势的试验论文，数据扎实但属区域性细分研究，公共影响有限。",[2405],{"name":2399,"url":2396},[32,34,503,2407,2408],"设施栽培","芹菜",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2412},{"candidates":2413,"agricultural_evidence":51},[2414,2418,2422],{"code":158,"score":397,"supported":51,"title":2415,"abstract":2416,"tags":2417},[159],[186,159],[503],{"code":62,"score":79,"supported":51,"title":2419,"abstract":2420,"tags":2421},[],[34,4],[34],{"code":78,"score":287,"supported":51,"title":2423,"abstract":2424,"tags":2425},[],[461],[32],[2427,2428],"圭亚那 芹菜 智能滴灌","IoT 灌溉 遮阴栽培","圭亚那芹菜智能滴灌-3540","10.18805\u002Fijare.af-1081",{"doi":2430,"openalex_id":2432,"authors":2433,"venue":2399,"cited_by_count":119,"oa_url":2396,"card":2440,"direction":128,"ingested_from":130},"W7214362492",[2434,2436,2438],{"name":2435,"orcid":17},"Joel Persaud",{"name":2437,"orcid":17},"Pawan Kumar Persaud",{"name":2439,"orcid":17},"Andrea Dookhu",{"tldr":2441,"method":2442,"finding":2443,"direction":128,"opportunity":2444},"研究验证旱季遮阴芹菜中智能滴灌能否保持病害防控与产量优势。","物联网自动灌溉系统，遮阴棚两处理对比试验，统计检验。","智能滴灌与喷灌产量无差异，但病害发生率显著更低（2.8% vs 9.3%）。","可探索智能滴灌在热带遮阴栽培中跨季节、跨作物的病害抑制机制与长期经济性。","2026-09-26T23:30:09.889320Z",{"id":2447,"title":2448,"url":2449,"summary":2450,"summary_zh":2451,"content":17,"source_name":2452,"source_url":2449,"published_at":2453,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2124,"score_detail":2454,"sources":2456,"tags":2458,"primary_topic":2460,"secondary_topics":2461,"classification":2462,"search_phrases":2491,"slug":2494,"view_count":205,"doi":2495,"paper":2496,"created_at":2511},3481,"ЦИФРОВІ ПІДХОДИ ДО УПРАВЛІННЯ ВОДНИМ РЕЖИМОМ ҐРУНТУ ЗА КРАПЛИННОГО ЗРОШЕННЯ ЯГІДНИХ КУЛЬТУР В УМОВАХ ЛІСОСТЕПУ УКРАЇНИ","https:\u002F\u002Fdoi.org\u002F10.32848\u002Fagrar.innov.2026.37.13","Мета статті – дослідження сучасних наукових підходів до підвищення ефективності управління водним режимом ґрунту за вирощування ягідних культур в умовах Лісостепу України із застосуванням цифрових технологій моніторингу та автоматизації поливів, а також оцінка їх впливу на оптимізацію водоспоживання, стабільність продуктивності насаджень і раціональне використання поливної води в системах краплинного зрошення. Особливу увагу приділено аналізу сучасних інструментів дистанційного контролю стану рослин і ґрунту, сенсорних систем визначення вологості, моделей планування режимів зрошення та інформаційно-аналітичних платформ підтримки прийняття рішень. Методи дослідження. У процесі досліджень використано методи системного аналізу, узагальнення, порівняння та інтерпретації результатів вітчизняних і зарубіжних наукових публікацій щодо управління водним режимом ґрунту ягідних культур. Проведено оцінку ефективності застосування сенсорів вологості ґрунту різних типів, супутникових індексів рослинності (NDVI), моделей розрахунку евапотранспірації культур за методикою FAO-56, а також автоматизованих систем керування поливами на основі IoT – технологій. Здійснено порівняльний аналіз показників водоспоживання, водопродуктивності та урожайності ягідних культур за різних режимів зрошення та рівнів цифровізації технологічних процесів. Результати дослідження. Встановлено, що застосування цифрових підходів до управління водним режимом ґрунту забезпечує підвищення ефективності використання поливної води на 20–35%, зменшення непродуктивних втрат вологи та стабілізацію вологості кореневмісного шару ґрунту протягом критичних фаз розвитку рослин. Обґрунтовано доцільність використання сенсорних систем контролю вологості ґрунту, супутникового моніторингу стану посівів і програмних моделей планування режимів зрошення для підвищення точності визначення строків і норм поливу. Показано, що інтеграція цифрових інструментів у технології краплинного зрошення сприяє підвищенню водопродуктивності насаджень ягідних культур, оптимізації режимів водоспоживання та зниженню енергетичних витрат на проведення поливів. Висновки. Узагальнення результатів досліджень свідчить про високу ефективність використання цифрових технологій моніторингу та управління водним режимом ґрунту у системах краплинного зрошення ягідних культур в умовах Лісостепу України. Використання сенсорних систем, супутникових індексів рослинності та автоматизованих алгоритмів керування поливами забезпечує підвищення рівня ресурсозбереження, стабільність урожайності та покращення екологічної ефективності технологій вирощування ягідних культур. Запровадження зазначених підходів доцільно розглядати як один із ключових напрямів удосконалення сучасних систем управління водними ресурсами в ягідництві. Отримані результати можуть бути використані при розробці рекомендацій щодо цифровізації управління зрошенням ягідних культур у господарствах Лісостепу України.","本文旨在研究在乌克兰森林草原条件下，利用数字监测技术和灌溉自动化提高浆果作物土壤水分管理效率的现代科学方法，并评估其对优化耗水量、种植园生产力稳定性以及滴灌系统中灌溉水合理利用的影响。特别关注对现代植物和土壤状态远程监控工具、湿度传感系统、灌溉制度规划模型及决策支持信息分析平台的分析。研究方法。在研究过程中，采用了系统分析、概括、比较以及解释国内外关于浆果作物土壤水分管理科学出版物的方法。评估了不同类型土壤湿度传感器、卫星植被指数（NDVI）、根据FAO-56方法计算作物蒸散量的模型以及基于物联网技术的灌溉自动化控制系统的应用效果。对不同灌溉制度和工艺过程数字化水平下浆果作物的耗水量、水分生产率和产量指标进行了比较分析。研究结果。已确定，采用数字方法管理土壤水分可确保灌溉水利用效率提高20–35%，减少无效水分损失，并在植物发育关键阶段稳定根层土壤湿度。论证了利用土壤湿度传感控制系统、作物状态卫星监测和灌溉制度规划软件模型以提高确定灌溉时间和灌溉定额精度的合理性。研究表明，将数字工具集成到滴灌技术中有助于提高浆果作物种植园的水分生产率、优化耗水制度并降低灌溉的能源消耗。结论。研究结果的综合表明，在乌克兰森林草原条件下的浆果作物滴灌系统中，利用数字监测和土壤水分管理技术具有高效率。使用传感系统、卫星植被指数和自动化灌溉控制算法可确保提高资源节","Аграрні інновації","2026-09-23T00:00:00Z",{"impact":174,"substance":616,"depth":24,"authority":141,"freshness":27,"relevant":8,"comment":2455},"乌克兰核心期刊论文，系统评估传感器、NDVI与IoT自动化灌溉对浆果滴灌水分管理的节水增产效果，方法清晰、数据具体，对智慧灌溉领域有参考价值。",[2457],{"name":2452,"url":2449},[32,34,622,257,2459],"滴灌",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2463},{"candidates":2464,"agricultural_evidence":51},[2465,2469,2473,2477,2483,2487],{"code":62,"score":79,"supported":51,"title":2466,"abstract":2467,"tags":2468},[],[34,4],[34],{"code":197,"score":287,"supported":51,"title":2470,"abstract":2471,"tags":2472},[],[200],[257],{"code":158,"score":205,"supported":51,"title":2474,"abstract":2475,"tags":2476},[],[186],[],{"code":2478,"score":205,"supported":51,"title":2479,"abstract":2480,"tags":2482},"I7",[],[2481],"研究方法",[],{"code":78,"score":8,"supported":22,"title":2484,"abstract":2485,"tags":2486},[],[],[32],{"code":633,"score":8,"supported":22,"title":2488,"abstract":2489,"tags":2490},[],[],[622],[2492,2493],"乌克兰 森林草原 浆果 滴灌","FAO-56 蒸散 滴灌 水分管理","乌克兰森林草原浆果滴灌-3481","10.32848\u002Fagrar.innov.2026.37.13",{"doi":2495,"openalex_id":2497,"authors":2498,"venue":2452,"cited_by_count":119,"oa_url":2505,"card":2506,"direction":128,"ingested_from":130},"W7214105593",[2499,2502],{"name":2500,"orcid":2501},"V. V. Karuna","https:\u002F\u002Forcid.org\u002F0009-0005-6162-5391",{"name":2503,"orcid":2504},"A. P. Shatkovskyi","https:\u002F\u002Forcid.org\u002F0000-0002-4366-0397","https:\u002F\u002Fagrarian-innovations.izpr.ks.ua\u002Findex.php\u002Fagrarian\u002Farticle\u002Fdownload\u002F1171\u002F1168",{"tldr":2507,"method":2508,"finding":2509,"direction":128,"opportunity":2510},"综述数字技术用于乌克兰森林草原浆果滴灌土壤水分管理，提升灌溉水利用效率20–35%。","系统分析文献，评估土壤湿度传感器、NDVI、FAO-56蒸散模型与IoT自动灌溉","数字监测与自动灌溉可节水20–35%，稳定根层湿度，提高水分生产力并降低能耗。","可在小浆果园区验证多源传感与FAO-56模型耦合的实时决策，填补东欧森林草原区数据空白。","2026-09-25T23:30:15.654633Z",{"id":2513,"title":2514,"url":2515,"summary":2516,"summary_zh":2517,"content":17,"source_name":2518,"source_url":2515,"published_at":2453,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":679,"score_detail":2519,"sources":2521,"tags":2523,"primary_topic":2525,"secondary_topics":2526,"classification":2528,"search_phrases":2551,"slug":2554,"view_count":287,"doi":2555,"paper":2556,"created_at":2568},3472,"Solar- Powered and Performance Evaluation of a Smart Irrigation System","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijemt.vol.12.no8.2026.pg158.171","Experiment was carried out on how to generate and developed solar powered pumping system for smart irrigation. Solar power energy materials used consist of photovoltaic panel, solar charge controller, batteries, and inverter, distribution pipe, voltmeter, wiring accessories and water pump. The coupled energy materials were set at an angle of 150 South - east. The generated voltage recorded during the day time from the solar energy were recorded in two parts; from solar panel and voltage controller. The two solar panels generated an average solar energy of 24.63v, PV panel and 10.23v charge controller. Within 10 hours set for solar energy assessment from the panels,from 9:00 am to 6:00pm, PV panels recorded voltage that ranged between 19.3 to 30.2, while Charge controller recorded from 5.1 to 16.0v.The voltage kept increasing from 9:0am until it reached a peak at 3:0pm, and started declining at 4:0pm to the lowest voltage at 6: 00pm, when the sun rays have gone dim.The charge controller allows current into battery and protect and regulate its charge and discharge. The pump efficiency was tested using variation in pumping time (sec), which was set at 10,20, 30,40,50 and 60 seconds. The flow rate (L\u002Fsec) remained constant at 5 L\u002Fsec, because the battery was at full charge state. The pumping unit was coupled to an automated soil moisture-based drip irrigation system, developed using smart irrigation system. Internet of Things (IoT) components used were, Arduino, Veroboard and Timer Switch, Soil Moisture Sensor, Relay Module and Water Pump. The experimental plots were laid out in an improvised green house. Med emitters were used as irrigation drippers. Moisture sensor was set to trigger irrigation when soil moisture was below50%. The, solar energy generated, sustained pumping water required for the Smart irrigation system throughout the irrigation scheduled period to harvest time.","本研究旨在探讨如何构建和开发用于智能灌溉的太阳能驱动抽水系统。所使用的太阳能能源材料包括光伏板、太阳能充电控制器、电池、逆变器、输水管道、电压表、接线附件和水泵。将耦合的能源材料以东南方向150°角安装。白天从太阳能中产生的电压记录分为两部分：来自太阳能板和电压控制器。两块太阳能板的平均太阳能发电量为24.63V，光伏板为24.63V，充电控制器为10.23V。在用于评估太阳能板发电量的10小时时段内（上午9：00至下午6：00），光伏板记录的电压范围为19.3至30.2V，而充电控制器记录的电压范围为5.1至16.0V。电压从上午9：00起持续上升，直至下午3：00达到峰值，下午4：00开始下降，至下午6：00阳光变暗时降至最低电压。充电控制器允许电流进入电池，并保护和调节其充放电。通过改变抽水时间（秒）测试水泵效率，设定时间为10、20、30、40、50和60秒。由于电池处于满充状态，流量（L\u002Fs）保持恒定在5L\u002Fs。抽水装置与基于土壤湿度的自动化滴灌系统耦合，该系统采用智能灌溉系统开发。所使用的物联网（IoT）组件包括Arduino、万用板（Veroboard）和定时开关、土壤湿度传感器、继电器模块和水泵。试验小区布置在简易温室中。采用中流量滴头作为灌溉滴头。湿度传感器设定为当土壤湿度低于50%时触发灌溉。所产生的太阳能在整个灌溉计划期间直至收获期，持续维持智能灌溉系统所需抽水量。","INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY",{"impact":27,"substance":498,"depth":141,"authority":87,"freshness":27,"relevant":8,"comment":2520},"太阳能驱动智能灌溉系统的实测数据与IoT集成方案有一定参考价值，但期刊层级偏低、影响范围有限，暂不建议进入每日精选。",[2522],{"name":2518,"url":2515},[32,34,384,1208,2524],"太阳能灌溉",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[2527],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":2529},{"candidates":2530,"agricultural_evidence":51},[2531,2535,2539,2543,2547],{"code":158,"score":397,"supported":51,"title":2532,"abstract":2533,"tags":2534},[159],[186,159],[384],{"code":62,"score":21,"supported":51,"title":2536,"abstract":2537,"tags":2538},[],[34,65,4],[34],{"code":197,"score":79,"supported":51,"title":2540,"abstract":2541,"tags":2542},[],[200,201],[1208],{"code":464,"score":205,"supported":51,"title":2544,"abstract":2545,"tags":2546},[],[577],[],{"code":78,"score":8,"supported":22,"title":2548,"abstract":2549,"tags":2550},[],[],[32],[2552,2553],"太阳能 智能灌溉 系统","土壤湿度 滴灌 物联网","太阳能智能灌溉系统-3472","10.56201\u002Fijemt.vol.12.no8.2026.pg158.171",{"doi":2555,"openalex_id":2557,"authors":2558,"venue":2518,"cited_by_count":119,"oa_url":2562,"card":2563,"direction":128,"ingested_from":130},"W7214070923",[2559],{"name":2560,"orcid":2561},"Malum Japhet Flayin","https:\u002F\u002Forcid.org\u002F0000-0002-9400-2546","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJEMT\u002FVOL. 12 NO. 8 2026\u002FSolar- Powered and Performance Evaluation 158-171.pdf",{"tldr":2564,"method":2565,"finding":2566,"direction":128,"opportunity":2567},"研发并评估太阳能供电的智能滴灌系统，利用土壤湿度传感器自动触发灌溉。","光伏板、充电控制器、电池、Arduino与土壤湿度传感器，温室试验。","光伏板日均电压24.63V，泵流量稳定5L\u002Fs，系统可全天候维持灌溉至收获。","可研究多传感器融合与能量管理优化，提升阴雨天续航及灌溉决策精度。","2026-09-25T23:30:10.959076Z",{"id":2570,"title":2571,"url":2572,"summary":2573,"summary_zh":17,"content":2574,"source_name":2575,"source_url":17,"published_at":2288,"category":2289,"cover_url":17,"hotness":21,"is_selected":22,"score":2576,"score_detail":2577,"sources":2579,"tags":2581,"primary_topic":2584,"secondary_topics":2585,"classification":2586,"search_phrases":2606,"slug":2609,"view_count":1203,"doi":17,"paper":17,"created_at":2610},3386,"龙江大地种田有多'聪明'？北大荒闫家岗农场72台套物联网设备构建'空天地人机'一体化数据网络","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm","北大荒集团闫家岗农场拥有72台套物联网设备，24小时不间断采集农业生产数据上传平台，通过AI分析给出生产建议。农场用几个关键词概括：'数字大脑'——巡田无人机已全面替代传统人工作业；'全天候守护'——实现气象、墒情、光照全方位智能监测；'智能装备'——农田水利'双孪生'系统实现远程调水、智能控温、虫情监测等功能。北大荒集团已在20多个农场实施智慧农业示范项目，已在七星、友谊等重点农场落地见效。","[![Image 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龙江大地种田有多“聪明”？在闫家岗农场解锁“丰收密码” \n\n时间：2026-09-24 来源：龙头新闻·黑龙江日报\n\n字号：大 中 小![Image 2](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_printIcon.png) 打印 \n\n分享:[](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm&title=%E9%BE%99%E6%B1%9F%E5%A4%A7%E5%9C%B0%E7%A7%8D%E7%94%B0%E6%9C%89%E5%A4%9A%E2%80%9C%E8%81%AA%E6%98%8E%E2%80%9D%EF%BC%9F%E5%9C%A8%E9%97%AB%E5%AE%B6%E5%B2%97%E5%86%9C%E5%9C%BA%E8%A7%A3%E9%94%81%E2%80%9C%E4%B8%B0%E6%94%B6%E5%AF%86%E7%A0%81%E2%80%9D \"qq空间\")[](javascript:void(0); \"微信\")[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm&title=%E9%BE%99%E6%B1%9F%E5%A4%A7%E5%9C%B0%E7%A7%8D%E7%94%B0%E6%9C%89%E5%A4%9A%E2%80%9C%E8%81%AA%E6%98%8E%E2%80%9D%EF%BC%9F%E5%9C%A8%E9%97%AB%E5%AE%B6%E5%B2%97%E5%86%9C%E5%9C%BA%E8%A7%A3%E9%94%81%E2%80%9C%E4%B8%B0%E6%94%B6%E5%AF%86%E7%A0%81%E2%80%9D \"新浪微博\")\n\n稻浪滚滚，锣鼓喧天，23日，在北大荒集团闫家岗农场2026年中国农民丰收节黑龙江省主场活动的现场，各界嘉宾、农民、市民游客与农场职工齐聚，处处洋溢着丰收的喜悦。在闫家岗七彩大道示范田边，几台物联网设备静静矗立，在热闹的节日中默默护航稻田，这是北大荒“聪明”种田的硬件设施，也是北大荒“空天地人机”一体化数据网络的神经末梢。\n\n站在金黄的水稻田边，北大荒集团黑龙江闫家岗农场智慧农业负责人郭腾接受了记者的采访。\n\n“你看到的我们身后的物联网设备，闫家岗农场拥有72台套，它可以实时24小时不间断采集田间的农业生产数据，上传到平台，通过AI分析给出农业生产建议，这些专业的生产建议可以通过手机传递到种植户手里。从凭经验种田到凭数据种粮，让更多种植户达到节本增收的效果。”\n\n闫家岗丰收节现场的无人收割机作业演示环节，不用人坐在驾驶室，机器自己就能在稻田里开展收割；AI病虫害识别技术也将亮相，不靠人工一遍遍下地巡查，借助智能手段就能快速捕捉田间病虫害线索，提前做好防控。\n\n当记者问到闫家岗农场在智慧农业建设方面取得了哪些进展时，郭腾笑着说：“智慧农业是我们闫家岗农场最亮的一张名片，也是我本人投入精力最多的领域。”\n\n他告诉记者，在闫家岗农场层面，用几个关键词来概括：第一个关键词是“数字大脑”。巡田无人机已经全面替代传统人工作业，实现了大面积、高效率的农田巡查。所有数据实时回传至田间监测系统后台，形成智慧农业的“数字大脑”，精准指导从种到收的每一个环节。第二个关键词是“全天候守护”。如今闫家岗的农田，实现了气象、墒情、光照的全方位智能监测，整套智能监测体系24小时不间断值守，气象风险、作物长势、土壤状态全部量化为精准数据，全程护航水稻稳产增收。第三个关键词是“智能装备”。农场建设了农田水利“双孪生”系统，实现远程调水、智能控温、虫情监测等功能，构建农情天空地感知网络。智能温室大棚、无人插秧机、智能灌溉等数字化设备已经成为标配。\n\n从垦区层面来看，北大荒集团的智慧农业建设已经形成了体系化的“北大荒方案”。\n\n记者了解到，北大荒集团已经在20多个农场实施了智慧农业示范项目，成功打造了可复制、可推广的“北大荒未来农场”技术模式，已在七星、友谊等重点农场落地见效，推动传统农耕从“会”种田向“慧”种田全面跨越。北大荒集团联合哈尔滨工业大学、东北农业大学共建了智慧农场技术与系统全国重点实验室，把科研攻关、系统集成、场景验证、规模推广串成一条闭环，让更多新技术从实验室走向田间。（黑龙江日报记者：刘畅）\n\n![Image 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\n即可将网页分享给您的微信好友或朋友圈。","中国农业农村信息网 2026-09-24",68,{"impact":24,"substance":174,"depth":498,"authority":835,"freshness":142,"relevant":8,"comment":2578},"以丰收节现场为切口，具体呈现北大荒闫家岗农场物联网、无人农机与数字大脑的落地成效，属省级媒体实地报道，有细节但偏宣传性，适合主题聚合而非头条精选。",[2580],{"name":2575,"url":2572},[32,33,34,2582,2583],"北大荒","无人农机",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2587},{"candidates":2588,"agricultural_evidence":51},[2589,2593,2597,2602],{"code":62,"score":251,"supported":51,"title":2590,"abstract":2591,"tags":2592},[34],[34],[34],{"code":78,"score":287,"supported":51,"title":2594,"abstract":2595,"tags":2596},[],[32],[32],{"code":2598,"score":8,"supported":22,"title":2599,"abstract":2600,"tags":2601},"F5",[],[],[2583],{"code":50,"score":8,"supported":22,"title":2603,"abstract":2604,"tags":2605},[],[],[33],[2607,2608],"北大荒 闫家岗农场 智慧农业","闫家岗农场 物联网设备","北大荒闫家岗农场智慧农业-3386","2026-09-25T00:09:27.560637Z",{"id":2612,"title":2613,"url":2614,"summary":2615,"summary_zh":2616,"content":17,"source_name":2617,"source_url":2614,"published_at":2618,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2619,"score_detail":2620,"sources":2622,"tags":2624,"primary_topic":2626,"secondary_topics":2627,"classification":2628,"search_phrases":2654,"slug":2657,"view_count":406,"doi":2658,"paper":2659,"created_at":2682},3163,"An NB-IoT-Based Architecture with Spatial-Statistical Analytics for Cross-Domain Air and Water Quality Monitoring in Aquaculture and Aquatic Environments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26185964","The rapid expansion of smart agriculture and precision aquaculture necessitates continuous, high-resolution environmental monitoring to optimize ecosystem stability and prevent catastrophic biomass loss. Conventional Internet of Things (IoT) solutions routinely treat atmospheric and aquatic parameters as isolated domains, neglecting the dynamic physicochemical coupling occurring across the air–water boundary layer. To overcome this domain fragmentation, this study presents an integrated edge-cloud telemetry architecture designed for concurrent, multi-domain environmental monitoring and cross-domain spatial-statistical analysis. The proposed framework employs low-cost, multi-sensor edge nodes integrated with Narrowband IoT (NB-IoT) cellular communication, achieving high signal penetration, energy-efficient operation, and direct base-station connectivity without local gateway dependencies. The system continuously acquires atmospheric parameters (temperature, relative humidity, particulate matter PM1.0\u002FPM2.5\u002FPM10, ozone O3, total volatile organic compounds TVOC, equivalent CO2, Air Quality Index AQI, and Ultraviolet Index UVI) alongside aquatic indicators (water temperature, pH, dissolved oxygen DO, electrical conductivity EC, and turbidity). Telemetry is streamed via Message Queuing Telemetry Transport (MQTT) to a centralized MySQL cloud database, providing real-time Grafana dashboards, spatial Inverse Distance Weighting (IDW) mapping, and automated multi-channel alerting via LINE Notify and email. The architecture was deployed and validated across the Tunghai University aquatic research facility, capturing n = 14,400 synchronized 1-min observations (with an initial raw Packet Delivery Rate of 99.24%). Statistical evaluations accounting for temporal autocorrelation (Neff≈892) and False Discovery Rate correction revealed significant cross-domain associations (padj\u003C0.001), notably an inverse association (r=−0.782, ρ=−0.794, τ=−0.612) between ambient air temperature and aquatic dissolved oxygen physically consistent with Henry’s Law of gas solubility, an inverse association (r=−0.763) between atmospheric humidity and dissolved oxygen, and a positive association (r=+0.789, ρ=+0.812) between humidity and aquatic turbidity. First-order partial correlation analysis (rTa,DO∣Tw=−0.172) confirmed that water temperature serves as the primary thermal mediator of dissolved oxygen depletion. By synergizing low-power NB-IoT telemetry with robust multi-domain analytics, this work provides a scalable, empirical foundation for transitioning from reactive threshold alerting to proactive predictive management in precision aquaculture.","智慧农业与精准水产养殖的快速扩张，亟需持续、高分辨率的环境监测，以优化生态系统稳定性并防止灾难性生物量损失。传统物联网（IoT）方案通常将大气与水体参数视为彼此孤立的领域，忽视了跨气–水边界层发生的动态物理化学耦合。为克服这种领域割裂，本研究提出了一种集成式边缘–云遥测架构，用于并发、多域环境监测与跨域空间统计分析。所提框架采用低成本多传感器边缘节点，并集成窄带物联网（NB-IoT）蜂窝通信，从而实现高信号穿透、节能运行以及无需本地网关依赖的直接基站连接。该系统持续采集大气参数（温度、相对湿度、颗粒物PM1.0\u002FPM2.5\u002FPM10、臭氧O3、总挥发性有机物TVOC、等效CO2、空气质量指数AQI和紫外线指数UVI）以及水体指标（水温、pH、溶解氧DO、电导率EC和浊度）。遥测数据通过消息队列遥测传输（MQTT）流式传输至集中式MySQL云数据库，提供实时Grafana仪表板、空间反距离加权（IDW）制图，以及通过LINE Notify和电子邮件实现的多通道自动告警。该架构已在东海大学水产研究设施中部署并验证，采集了n = 14,400条同步1分钟观测数据（初始原始数据包投递率为99.24%）。考虑时间自相关（Neff≈892）和错误发现率校正的统计评估揭示了显著的跨域关联（padj\u003C0.001），尤其是环境气温与水体溶解氧之间的负相关（r=−0.782，ρ=−0.794，τ=−0.612），这在物理上与亨利气体溶解度定律一致；大气湿度与溶解氧之间的负相关（r=−0.763）；以及湿度与水体浊度之间的正相关（r=+0.789，ρ=+0.812）。一阶偏相关分析（rTa,DO∣Tw=−0.172）证实，水温是溶解氧耗竭的主要热中介因素。通过将低功耗NB-IoT遥测与稳健的多域分析相结合，该","Sensors","2026-09-20T00:00:00Z",72,{"impact":21,"substance":25,"depth":24,"authority":141,"freshness":142,"relevant":8,"comment":2621},"NB-IoT边缘云架构实现空气-水质跨域同步监测，1.44万条实测数据与统计结论扎实，对精准水产养殖有参考价值，但属单点验证性研究，产业影响有限。",[2623],{"name":2617,"url":2614},[32,34,750,751,2625],"NB-IoT",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2629},{"candidates":2630,"agricultural_evidence":51},[2631,2635,2640,2644,2650],{"code":62,"score":24,"supported":51,"title":2632,"abstract":2633,"tags":2634},[4],[34,65,4],[34],{"code":772,"score":397,"supported":51,"title":2636,"abstract":2638,"tags":2639},[2637],"aquaculture",[775,2637],[750],{"code":78,"score":79,"supported":51,"title":2641,"abstract":2642,"tags":2643},[],[32,461],[32],{"code":2645,"score":205,"supported":51,"title":2646,"abstract":2647,"tags":2649},"D5",[],[2648],"precision aquaculture",[],{"code":784,"score":8,"supported":22,"title":2651,"abstract":2652,"tags":2653},[],[],[751],[2655,2656],"NB-IoT 水产养殖 水质监测","东海大学 空气质量 溶解氧","NB-IoT水产养殖水质监测-3163","10.3390\u002Fs26185964",{"doi":2658,"openalex_id":2660,"authors":2661,"venue":2617,"cited_by_count":119,"oa_url":2614,"card":2677,"direction":128,"ingested_from":130},"W7213982361",[2662,2665,2668,2671,2674],{"name":2663,"orcid":2664},"Tsai-Chen Yang","https:\u002F\u002Forcid.org\u002F0009-0007-2271-6690",{"name":2666,"orcid":2667},"Yin-Tzu Huang","https:\u002F\u002Forcid.org\u002F0000-0001-8679-091X",{"name":2669,"orcid":2670},"Yu‐Fang Chung","https:\u002F\u002Forcid.org\u002F0000-0002-7373-7201",{"name":2672,"orcid":2673},"Tzer‐Shyong Chen","https:\u002F\u002Forcid.org\u002F0000-0001-8915-5057",{"name":2675,"orcid":2676},"Chao‐Tung Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9579-4426",{"tldr":2678,"method":2679,"finding":2680,"direction":128,"opportunity":2681},"提出NB-IoT边缘云架构，同步监测空气与水质并做跨域空间统计分析。","低成本多传感器NB-IoT节点、MQTT+MySQL云、IDW空间映射与偏相关分","气温与溶解氧显著负相关，水温是溶解氧消耗的主要热中介。","可探索跨域耦合预警模型，将气水界面关联用于养殖缺氧风险提前预测。","2026-09-22T23:30:12.820712Z",{"id":2684,"title":2685,"url":2686,"summary":2687,"summary_zh":2688,"content":17,"source_name":2689,"source_url":2686,"published_at":2690,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2691,"score_detail":2692,"sources":2694,"tags":2696,"primary_topic":2697,"secondary_topics":2698,"classification":2701,"search_phrases":2724,"slug":2727,"view_count":1203,"doi":2728,"paper":2729,"created_at":2743},3158,"Internet of things-based smart irrigation system using soil moisture and weather data","https:\u002F\u002Fdoi.org\u002F10.12928\u002Ftelkomnika.v24i5.27889","Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.","农业中的水资源浪费仍然是一项重大挑战，因为灌溉实践往往依赖固定时间表，而非实际田间条件。本研究提出了一种基于物联网（IoT）的智能灌溉系统，旨在通过实时监测和自动灌溉控制来提高用水效率。该系统将电容式土壤湿度传感器与从在线应用程序编程接口（API）获取的天气信息相结合，同时所有数据处理均在Raspberry Pi边缘设备上本地完成。系统采用基于规则的决策机制，将土壤状况分为干燥、适宜和湿润三类，并根据土壤湿度水平和降雨预报确定适当的灌溉措施。该系统使用低成本和易于获取的组件实现，并在受控条件下进行了测试，土壤湿度水平范围约为0%至85%以上。实验结果表明，系统能够一致地分类临界干燥、适宜和临界湿润状况，从而在不同情景下实现适当的灌溉响应。此外，电子邮件通知仅在临界状况下生成，而在适宜湿度水平下未触发任何警报，表明系统运行稳定可靠。所提出的系统为支持高效灌溉管理和可持续农业实践提供了一种实用且具有成本效益的解决方案。","TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z",58,{"impact":27,"substance":174,"depth":498,"authority":251,"freshness":27,"relevant":8,"comment":2693},"低成本物联网智能灌溉系统，方法清晰、结论可靠，对节水农业有实用参考价值，但属常规技术验证类论文，影响范围有限。",[2695],{"name":2689,"url":2686},[32,34,503,1208,1209],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[2699,2700],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":2702},{"candidates":2703,"agricultural_evidence":51},[2704,2708,2712,2716,2720],{"code":158,"score":1150,"supported":51,"title":2705,"abstract":2706,"tags":2707},[159],[186,187,159],[503,1209],{"code":62,"score":24,"supported":51,"title":2709,"abstract":2710,"tags":2711},[65],[34,65,4],[34],{"code":197,"score":397,"supported":51,"title":2713,"abstract":2714,"tags":2715},[201],[200,201],[1208],{"code":926,"score":205,"supported":51,"title":2717,"abstract":2718,"tags":2719},[],[929],[],{"code":78,"score":8,"supported":22,"title":2721,"abstract":2722,"tags":2723},[],[],[32],[2725,2726],"IoT 智能灌溉 土壤湿度","Raspberry Pi 边缘计算 灌溉","IoT智能灌溉土壤湿度-3158","10.12928\u002Ftelkomnika.v24i5.27889",{"doi":2728,"openalex_id":2730,"authors":2731,"venue":2689,"cited_by_count":119,"oa_url":2686,"card":2738,"direction":128,"ingested_from":130},"W7213907872",[2732,2734,2736],{"name":2733,"orcid":17},"Zakarie Abdi Mohamud",{"name":2735,"orcid":17},"Rozeha Binti A. Rashid",{"name":2737,"orcid":17},"Yazid Abubakar Sufyan",{"tldr":2739,"method":2740,"finding":2741,"direction":128,"opportunity":2742},"基于物联网与土壤湿度及天气数据，实现低成本自动灌溉决策系统。","电容式土壤湿度传感器、天气API、Raspberry Pi边缘计算与规则决策。","系统能准确分类干、适宜、湿状态，仅在临界条件触发灌溉与邮件通知。","可引入机器学习预测土壤湿度动态，优化规则阈值并扩展至多作物多区域验证。","2026-09-22T23:30:10.956765Z",{"id":2745,"title":2746,"url":2747,"summary":2748,"summary_zh":17,"content":17,"source_name":2749,"source_url":2747,"published_at":2750,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1459,"score_detail":2751,"sources":2753,"tags":2755,"primary_topic":2757,"secondary_topics":2759,"classification":2762,"search_phrases":2785,"slug":2788,"view_count":287,"doi":2789,"paper":2790,"created_at":2798},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture","2026-09-22T00:00:00Z",{"impact":251,"substance":24,"depth":174,"authority":141,"freshness":21,"relevant":8,"comment":2752},"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[2754],{"name":2749,"url":2747},[32,33,2756,34,97],"产量预测",{"code":94,"slug":98,"name":97,"path":2758,"parent_code":1020},"\u002Ftopic\u002Ffield-crops\u002Fmaize",[2760,2761],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":2763},{"candidates":2764,"agricultural_evidence":51},[2765,2769,2773,2777,2781],{"code":94,"score":251,"supported":51,"title":2766,"abstract":2767,"tags":2768},[98],[98],[97],{"code":78,"score":251,"supported":51,"title":2770,"abstract":2771,"tags":2772},[461],[461],[32],{"code":50,"score":251,"supported":51,"title":2774,"abstract":2775,"tags":2776},[171],[171],[33],{"code":62,"score":251,"supported":51,"title":2778,"abstract":2779,"tags":2780},[4],[4],[34],{"code":633,"score":8,"supported":22,"title":2782,"abstract":2783,"tags":2784},[],[],[2756],[2786,2787],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":2789,"openalex_id":2791,"authors":2792,"venue":2749,"cited_by_count":119,"oa_url":2747,"card":17,"direction":128,"ingested_from":130},"W7213978365",[2793,2796],{"name":2794,"orcid":2795},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":2797,"orcid":17},"Yasinta Nzogera","2026-09-22T23:30:10.178097Z",{"id":2800,"title":2801,"url":2802,"summary":2803,"summary_zh":2804,"content":17,"source_name":2805,"source_url":2802,"published_at":2806,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2619,"score_detail":2807,"sources":2809,"tags":2811,"primary_topic":2812,"secondary_topics":2813,"classification":2816,"search_phrases":2848,"slug":2851,"view_count":406,"doi":2852,"paper":2853,"created_at":2870},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",{"impact":251,"substance":616,"depth":26,"authority":141,"freshness":142,"relevant":8,"comment":2808},"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[2810],{"name":2805,"url":2802},[32,33,170,34,503],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[2814,2815],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":2817},{"candidates":2818,"agricultural_evidence":51},[2819,2823,2827,2831,2835,2840,2844],{"code":158,"score":24,"supported":51,"title":2820,"abstract":2821,"tags":2822},[159],[186,187,159],[503],{"code":50,"score":141,"supported":51,"title":2824,"abstract":2825,"tags":2826},[58],[170],[33,170],{"code":62,"score":142,"supported":51,"title":2828,"abstract":2829,"tags":2830},[4],[],[34],{"code":197,"score":87,"supported":51,"title":2832,"abstract":2833,"tags":2834},[],[200,201],[],{"code":190,"score":205,"supported":51,"title":2836,"abstract":2837,"tags":2839},[],[2838],"干旱",[],{"code":926,"score":205,"supported":51,"title":2841,"abstract":2842,"tags":2843},[],[929],[],{"code":78,"score":8,"supported":22,"title":2845,"abstract":2846,"tags":2847},[],[],[32],[2849,2850],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":2852,"openalex_id":2854,"authors":2855,"venue":2805,"cited_by_count":119,"oa_url":2802,"card":2865,"direction":128,"ingested_from":130},"W7213546497",[2856,2859,2862],{"name":2857,"orcid":2858},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":2860,"orcid":2861},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":2863,"orcid":2864},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":2866,"method":2867,"finding":2868,"direction":128,"opportunity":2869},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":2872,"title":2873,"url":2874,"summary":2875,"summary_zh":2876,"content":17,"source_name":1006,"source_url":2874,"published_at":2877,"category":20,"cover_url":17,"hotness":2878,"is_selected":22,"score":2879,"score_detail":2880,"sources":2882,"tags":2888,"primary_topic":2891,"secondary_topics":2892,"classification":2893,"search_phrases":2908,"slug":2911,"view_count":406,"doi":2912,"paper":2913,"created_at":2925},2865,"AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809198","Abstract The proliferation of Internet of Things (IoT) devices in smart cities, industrial automation, and healthcare monitoring has created an unprecedented demand for sustainable energy solutions. Traditional battery-powered IoT deployments face significant challenges including limited operational lifespan, environmental hazards from battery disposal, and high maintenance costs associated with manual replacement. This study presents a novel AI-enabled wireless power transfer (WPT) architecture that leverages machine learning algorithms to optimize energy delivery, predict device energy demands, and autonomously manage power distribution across large-scale IoT ecosystems. We propose a three-tier architecture comprising (1) an intelligent RF energy harvesting layer with adaptive rectenna arrays, (2) a reinforcement learning-based power allocation engine, and (3) a cloud-native energy orchestration platform. Through extensive simulation and prototype validation across three distinct IoT scenarios smart agriculture, industrial asset monitoring, and wearable health networks we demonstrate that the proposed system achieves 78.4% energy transfer efficiency (a 34% improvement over conventional directional WPT), reduces network energy waste by 62%, and extends device operational lifespan by 4.7× compared to battery-dependent counterparts. Our findings establish that AI-driven dynamic optimization of WPT parameters including beam-forming angles, transmission power, and duty cycling enables scalable, sustainable IoT deployments that were previously infeasible. This research contributes to the emerging field of intelligent energy harvesting networks and provides a replicable framework for next-generation green IoT infrastructure. Keywords: wireless power transfer, Internet of Things, machine learning, energy harvesting, sustainable computing, reinforcement learning, smart cities, green communication. References Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023.3245678 Ku, M.-L., Li, W., Chen, Y., & Liu, K. J. R. (2016). Advances in energy harvesting communications: Past, present, and future challenges. IEEE Communications Surveys & Tutorials, 18(2), 1384–1412. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2015.2497328 Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2021). Wireless charger networking for mobile devices: Fundamentals, standards, and applications. IEEE Wireless Communications, 22(2), 32–41. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMWC.2015.7091061 R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.1) [Computer software]. R Foundation for Statistical Computing. https:\u002F\u002Fwww.R-project.org\u002F Statista. (2023). Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2030. https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F1183457\u002Fiot-connected-devices-worldwide\u002F Tran, N. H., Hoang, D. T., Niyato, D., Nguyen, C. M., & Han, Z. (2021). The roadmap to 6G: AI-empowered wireless networks. IEEE Communications Magazine, 59(1), 112–117. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMCOM.001.2000406 United Nations Environment Programme. (2022). Global e-waste monitor 2022: Electronic waste management in the circular economy. United Nations Publications. Zhang, J., Guo, H., & Liu, H. (2022). Intelligent reflecting surface aided wireless power transfer for IoT devices. IEEE Transactions on Communications, 70(5), 3381–3396. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCOMM.2022.3156789 How to Cite Lucky Joseph, O., & Osaremwinda, O. (2026). AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS. GPH-International Journal of Computer Science and Engineering, 9(1), 176-188. https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199","摘要 物联网（IoT）设备在智慧城市、工业自动化和医疗健康监测中的激增，催生了对可持续能源解决方案前所未有的需求。传统的电池供电物联网部署面临诸多重大挑战，包括运行寿命有限、电池处置带来的环境危害，以及人工更换电池所产生的高昂维护成本。本研究提出了一种新颖的人工智能赋能无线能量传输（WPT）架构，利用机器学习算法优化能量传输、预测设备能量需求，并在大规模物联网生态系统中自主管理功率分配。我们提出了一种三层架构，包括：（1）具有自适应整流天线阵列的智能射频能量收集层；（2）基于强化学习的功率分配引擎；（3）云原生能量编排平台。通过在三种不同物联网场景——智慧农业、工业资产监测和可穿戴健康网络——中进行大量仿真和原型验证，我们证明所提出的系统实现了78.4%的能量传输效率（较传统定向WPT提升34%），将网络能量浪费降低了62%，并将设备运行寿命较依赖电池的同类设备延长了4.7倍。我们的研究结果表明，对WPT参数（包括波束成形角度、发射功率和占空比）进行人工智能驱动的动态优化，能够实现此前不可行的大规模、可持续物联网部署。本研究为智能能量收集网络这一新兴领域作出了贡献，并为下一代绿色物联网基础设施提供了可复制的框架。关键词：无线能量传输，物联网，机器学习，能量收集，可持续计算，强化学习，智慧城市，绿色通信。参考文献 Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023","2026-09-17T00:00:00Z",40,77,{"impact":24,"substance":25,"depth":24,"authority":21,"freshness":142,"relevant":8,"comment":2881},"论文提出AI驱动的无线能量传输三层架构，在智慧农业等场景验证能效提升34%、设备寿命延长4.7倍，对农业物联网可持续供电有参考价值，但属仿真与原型验证阶段，产业落地尚早。",[2883,2884,2886],{"name":1006,"url":2874},{"name":1006,"url":2885},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199",{"name":1006,"url":2887},"https:\u002F\u002Fwww.gphjournal.org\u002Findex.php\u002Fcse\u002Farticle\u002Fview\u002F2581",[32,33,34,2889,2890],"无线能量传输","绿色通信",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2894},{"candidates":2895,"agricultural_evidence":51},[2896,2900,2904],{"code":62,"score":24,"supported":51,"title":2897,"abstract":2898,"tags":2899},[4],[34,65,4],[34],{"code":78,"score":79,"supported":51,"title":2901,"abstract":2902,"tags":2903},[],[32,461],[32],{"code":50,"score":79,"supported":51,"title":2905,"abstract":2906,"tags":2907},[],[170,171],[33],[2909,2910],"AI 无线能量传输 物联网","智能农业 能量采集 可持续","AI无线能量传输物联网-2865","10.5281\u002Fzenodo.22809198",{"doi":2912,"openalex_id":2914,"authors":2915,"venue":1006,"cited_by_count":119,"oa_url":2874,"card":2920,"direction":128,"ingested_from":130},"W7213495899",[2916,2918],{"name":2917,"orcid":17},"Lucky Joseph Ogbogbo",{"name":2919,"orcid":17},"Osaremwinda OMOROGIUWA",{"tldr":2921,"method":2922,"finding":2923,"direction":128,"opportunity":2924},"提出AI驱动的无线供电三层架构，优化大规模IoT设备能量传输与分配。","强化学习功率分配、自适应整流天线阵列与云原生编排平台仿真验证。","能量传输效率达78.4%，浪费减少62%，设备寿命延长4.7倍。","可将该WPT架构落地农田传感器网络，研究作物环境下的能量预测与波束自适应优化。","2026-09-18T23:30:08.902438Z",{"id":2927,"title":2928,"url":2929,"summary":2930,"summary_zh":2931,"content":17,"source_name":2932,"source_url":2929,"published_at":2933,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":551,"score_detail":2934,"sources":2936,"tags":2938,"primary_topic":2940,"secondary_topics":2941,"classification":2942,"search_phrases":2961,"slug":2964,"view_count":205,"doi":2965,"paper":2966,"created_at":2985},2777,"Satellite Positioning-Based Precision Irrigation and Crop Health Monitoring System","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1577","Precision agriculture requires timely information on spatial variations in soil and environmental conditions to improve irrigation efficiency and crop productivity. This paper presents a low-cost satellite positioning-based precision irrigation and crop health monitoring system using an ESP32 microcontroller, soil-moisture sensor, DHT22 temperature and relative-humidity sensor, light-intensity sensor, NPK sensor, pH sensor, and NEO-6 GPS receiver. The system acquires soil, environmental, nutrient, and positional data and associates each measurement with its geographical coordinates. Soil moisture is used as the primary parameter for irrigation control, while temperature, relative humidity, light intensity, NPK, and pH provide supplementary indicators of crop and soil conditions. The GPS receiver enables spatial tagging of measurements for location-specific field monitoring and irrigation decisions. The results demonstrate sensor-based irrigation operation, spatial variability in field conditions, GPS positioning within approximately 3–5 m under open-sky conditions, and 29.3% reduction in water consumption compared with fixed-time irrigation. The proposed system provides a low-cost platform for spatially informed irrigation and crop-condition monitoring, with potential application in small- and medium-scale agricultural fields.","精准农业需要及时获取土壤和环境条件的空间变异信息，以提高灌溉效率和作物生产力。本文提出了一种基于低成本卫星定位的精准灌溉与作物健康监测系统，采用ESP32微控制器、土壤湿度传感器、DHT22温湿度传感器、光照强度传感器、NPK传感器、pH传感器和NEO-6 GPS接收器。该系统采集土壤、环境、养分和位置数据，并将每次测量与其地理坐标关联。土壤湿度作为灌溉控制的主要参数，而温度、相对湿度、光照强度、NPK和pH则提供作物和土壤条件的辅助指标。GPS接收器实现测量的空间标记，以支持特定位置的田间监测和灌溉决策。结果表明，基于传感器的灌溉操作可行，田间条件存在空间变异，在开阔天空条件下GPS定位精度约为3–5 m，与固定时间灌溉相比用水量减少了29.3%。所提出的系统为空间信息驱动的灌溉和作物状况监测提供了一个低成本平台，具有在中小型农田中应用的潜力。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-15T00:00:00Z",{"impact":27,"substance":24,"depth":397,"authority":87,"freshness":142,"relevant":8,"comment":2935},"低成本ESP32+GPS精准灌溉系统，实测节水29.3%，方法具体数据可信，但属实验室\u002F小规模验证，产业影响有限，可作为智慧农业技术案例入选。",[2937],{"name":2932,"url":2929},[32,34,1650,1582,2939],"卫星定位",{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":2943},{"candidates":2944,"agricultural_evidence":51},[2945,2949,2953,2957],{"code":158,"score":397,"supported":51,"title":2946,"abstract":2947,"tags":2948},[159],[186,159],[1650],{"code":78,"score":79,"supported":51,"title":2950,"abstract":2951,"tags":2952},[],[82,83],[32],{"code":197,"score":87,"supported":51,"title":2954,"abstract":2955,"tags":2956},[],[200,201],[],{"code":62,"score":8,"supported":22,"title":2958,"abstract":2959,"tags":2960},[],[],[34],[2962,2963],"卫星定位 土壤监测 智慧农业 精准灌溉","卫星定位 土壤监测","卫星定位土壤监测智慧农业精准灌溉-2777","10.38124\u002Fijisrt\u002F26aug1577",{"doi":2965,"openalex_id":2967,"authors":2968,"venue":2932,"cited_by_count":119,"oa_url":2979,"card":2980,"direction":128,"ingested_from":130},"W7213362841",[2969,2971,2973,2975,2977],{"name":2970,"orcid":17},"O. A. Akintola",{"name":2972,"orcid":17},"A. N. Lawal",{"name":2974,"orcid":17},"O. B. Goodtalk",{"name":2976,"orcid":17},"J.O. Ayankale",{"name":2978,"orcid":17},"I. Mafiana","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1577.pdf",{"tldr":2981,"method":2982,"finding":2983,"direction":128,"opportunity":2984},"开发基于ESP32和GPS的低成本精准灌溉与作物健康监测系统，实现空间标记与节水。","ESP32、土壤湿度、DHT22、光照、NPK、pH、NEO-6 GPS，以土壤","GPS定位精度3-5米，相比定时灌溉节水29.3%，并揭示田间空间变异。","可研究多传感器融合的智能决策算法，或结合低功耗广域网实现大田远程监测与精准灌溉。","2026-09-17T23:30:18.174616Z",{"id":2987,"title":2988,"url":2989,"summary":2990,"summary_zh":2991,"content":17,"source_name":2992,"source_url":2989,"published_at":2993,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":679,"score_detail":2994,"sources":2996,"tags":2998,"primary_topic":17,"secondary_topics":3000,"classification":3001,"search_phrases":3016,"slug":3019,"view_count":287,"doi":3020,"paper":3021,"created_at":3040},2771,"Edge-AI Based Smart Pet Monitoring Framework: A Rabbit Case Study","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208534-459","Continuous Monitoring of animals is hard when the people who take care of them are not around.Changes in how they are eaten, how much they drink, the way they stand and what they do during the day can show if their health is changing or if their normal routine is different.This paper talks about an Edge AI-based Smart Pet Monitoring Framework.The goal is watching the pet's behaviour and the environment around them in time.The system keeps privacy in mind.It uses computer vision, IoT, sensors and computing that happens close to the data not in the cloud This reduces the need for internet access.A Raspberry Pi serves as the edge device running a Python and OpenCV image processing chain that uses a YOLOv9 detector to spot feeding, drinking, posture and activity states.Temperature, humidity, ultrasonic distance and water-level sensors give environment data while an Arduino control layer handles alerts using an LCD, buzzer, LEDs and a servo.Local inference gives real-time processing and improves privacy, data ownership and response time.We tested than 200 images in a controlled setting and found it works but the test is still early.This framework offers a flexible base for smart pet monitoring, which could help at home in shelters and, for vets once more tests are done.","当照顾动物的人不在身边时，对动物进行持续监测是很困难的。它们进食方式、饮水量、站立姿态以及白天活动的变化，可以反映其健康状况是否发生变化或日常规律是否出现异常。本文讨论了一种基于边缘人工智能的智能宠物监测框架。其目标是及时观察宠物的行为及其周围环境。该系统注重隐私保护。它使用计算机视觉、物联网、传感器以及靠近数据端而非云端进行的计算，这减少了对互联网接入的需求。树莓派作为边缘设备，运行Python和OpenCV图像处理流程，并使用YOLOv9检测器识别进食、饮水、姿态和活动状态。温度、湿度、超声波距离和水位传感器提供环境数据，而Arduino控制层通过LCD、蜂鸣器、LED和舵机处理警报。本地推理实现了实时处理，并改善了隐私、数据所有权和响应时间。我们在受控环境中测试了200多张图像，发现系统可以工作，但测试仍处于早期阶段。该框架为智能宠物监测提供了一个灵活的基础，在完成更多测试后，可能有助于家庭、收容所以及兽医使用。","International Journal of Innovative Research in Technology","2026-09-16T00:00:00Z",{"impact":27,"substance":498,"depth":141,"authority":1203,"freshness":142,"relevant":8,"comment":2995},"边缘AI宠物监测框架，与农业信息化关联偏弱且测试样本仅200张，属早期探索性论文，不宜进入每日精选。",[2997],{"name":2992,"url":2989},[32,33,181,34,2999],"动物监测",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":3002},{"candidates":3003,"agricultural_evidence":22,"reason":347},[3004,3008,3012],{"code":62,"score":27,"supported":51,"title":3005,"abstract":3006,"tags":3007},[],[34,4],[34,181],{"code":50,"score":79,"supported":51,"title":3009,"abstract":3010,"tags":3011},[],[56,59],[33],{"code":78,"score":8,"supported":22,"title":3013,"abstract":3014,"tags":3015},[],[],[32],[3017,3018],"农业人工智能 动物监测 智慧农业 边缘计算","农业人工智能 动物监测","农业人工智能动物监测智慧农业边缘计算-2771","10.64643\u002Fijirt.208534-459",{"doi":3020,"openalex_id":3022,"authors":3023,"venue":2992,"cited_by_count":119,"oa_url":3034,"card":3035,"direction":128,"ingested_from":130},"W7213273522",[3024,3026,3028,3030,3032],{"name":3025,"orcid":17},"Vinit Masale",{"name":3027,"orcid":17},"Suyog Mamankar",{"name":3029,"orcid":17},"Prathamesh Sawant",{"name":3031,"orcid":17},"Mhaboob Ali",{"name":3033,"orcid":17},"Prof. Jyoti Shrote","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":3036,"method":3037,"finding":3038,"direction":128,"opportunity":3039},"提出基于边缘AI的宠物监测框架，用树莓派和YOLOv9识别兔子行为与环境。","树莓派边缘计算、YOLOv9、OpenCV、Arduino及温湿度超声波传感器。","200张图像测试验证了框架可行性，但需更多测试才能实际应用。","可扩展至畜禽行为健康监测，解决边缘设备算力与多目标识别精度问题。","2026-09-17T23:30:10.728310Z",{"id":3042,"title":3043,"url":3044,"summary":3045,"summary_zh":3046,"content":17,"source_name":3047,"source_url":3044,"published_at":2877,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":3048,"score_detail":3049,"sources":3051,"tags":3053,"primary_topic":3054,"secondary_topics":3055,"classification":3057,"search_phrases":3076,"slug":3079,"view_count":406,"doi":3080,"paper":3081,"created_at":3093},2767,"Prototype Sistem Kendali Penyiraman Tanaman Otomatis Berbasis IoT dengan Mode Manual dan Otomatis Menggunakan ESP32 dan Platform Blynk","https:\u002F\u002Fdoi.org\u002F10.61132\u002Fjupiter.v4i5.1609","The increasing scarcity of water resources and the need for optimal soil moisture levels for plant growth have driven the development of smart irrigation systems based on the Internet of Things (IoT). This study presents a prototype of an automatic plant watering control system using an ESP32 microcontroller equipped with a capacitive soil moisture sensor, relay module, and I2C LCD interface. The system is designed with two operating modes: an automatic mode that independently activates the water pump based on soil moisture thresholds, and a manual mode that allows users to control the pump directly through the Blynk application on a smartphone. Sensor data is transmitted to the Blynk platform in real-time via a WiFi connection using virtual pin communication protocols. Test results show that the system is capable of reading soil moisture values with good accuracy, responding to changes in soil conditions in less than 2 seconds, and simultaneously displaying moisture status and pump conditions on both the LCD and the Blynk application. This system is expected to serve as a solution for efficient water use in urban and household agriculture, supporting the concept of sustainable and energy-efficient farming.","水资源日益稀缺以及植物生长对最佳土壤湿度的需求，推动了基于物联网（IoT）的智能灌溉系统的发展。本研究提出了一种自动植物浇水控制系统的原型，该系统采用ESP32微控制器，并配备电容式土壤湿度传感器、继电器模块和I2C液晶显示接口。系统设计了两种运行模式：自动模式根据土壤湿度阈值独立启动水泵，手动模式允许用户通过智能手机上的Blynk应用程序直接控制水泵。传感器数据通过WiFi连接，利用虚拟引脚通信协议实时传输至Blynk平台。测试结果表明，该系统能够以良好的精度读取土壤湿度值，在2秒内响应土壤条件变化，并同时在液晶显示屏和Blynk应用程序上显示湿度状态和水泵运行情况。该系统有望为城市和家庭农业中的高效用水提供解决方案，支持可持续和节能农业的理念。","Jupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika",50,{"impact":27,"substance":498,"depth":141,"authority":1203,"freshness":21,"relevant":8,"comment":3050},"基于ESP32与Blynk的自动灌溉原型系统，方法常规、规模有限，属细分技术验证，公共价值与权威性一般，不宜进入每日精选。",[3052],{"name":3047,"url":3044},[32,34,503,1208,1209],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[3056],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":3058},{"candidates":3059,"agricultural_evidence":51},[3060,3064,3068,3072],{"code":62,"score":24,"supported":51,"title":3061,"abstract":3062,"tags":3063},[4],[34,65,4],[34],{"code":158,"score":835,"supported":51,"title":3065,"abstract":3066,"tags":3067},[],[186,187,159],[503,1209],{"code":197,"score":79,"supported":51,"title":3069,"abstract":3070,"tags":3071},[],[200,201],[1208],{"code":78,"score":8,"supported":22,"title":3073,"abstract":3074,"tags":3075},[],[],[32],[3077,3078],"土壤墒情 智慧农业 智能灌溉 节水农业","土壤墒情 智慧农业","土壤墒情智慧农业智能灌溉节水农业-2767","10.61132\u002Fjupiter.v4i5.1609",{"doi":3080,"openalex_id":3082,"authors":3083,"venue":3047,"cited_by_count":119,"oa_url":3044,"card":3088,"direction":128,"ingested_from":130},"W7213462460",[3084,3086],{"name":3085,"orcid":17},"Rifki Aldiansyah",{"name":3087,"orcid":17},"Dani Sasmoko",{"tldr":3089,"method":3090,"finding":3091,"direction":128,"opportunity":3092},"基于ESP32和Blynk开发了带手动\u002F自动模式的自动浇水原型系统。","ESP32、电容式土壤湿度传感器、继电器、I2C LCD与Blynk物联网平台。","系统能准确读取土壤湿度，响应时间小于2秒，并实时同步显示状态。","可扩展多传感器融合与自适应阈值算法，提升灌溉决策的精准性和节能性。","2026-09-17T23:30:10.351496Z",{"id":3095,"title":3096,"url":3097,"summary":3098,"summary_zh":3099,"content":17,"source_name":2932,"source_url":3097,"published_at":3100,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":818,"score_detail":3101,"sources":3103,"tags":3105,"primary_topic":3107,"secondary_topics":3108,"classification":3109,"search_phrases":3124,"slug":3126,"view_count":1203,"doi":3127,"paper":3128,"created_at":3145},2654,"Design and Implementation of a Low-Cost GNSSBased Precision Agricultural Monitoring System with Real-Time IoT Visualization","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1576","Precision agriculture provides an effective approach for improving agricultural productivity through spatially and temporally informed monitoring of soil, environmental, and crop conditions. However, many precision agriculture technologies remain relatively expensive and technically complex for small- and medium-scale farmers, particularly in developing countries. This paper presents a low-cost GNSS-based precision agricultural monitoring system integrating an ESP32, agricultural sensors, IoT communication, and real-time web visualization. The system acquires soil moisture, temperature, relative humidity, and GNSS data and transmits location-tagged measurements to a web server for remote monitoring.","精准农业通过基于空间和时间信息的土壤、环境及作物状况监测，为提高农业生产率提供了有效途径。然而，许多精准农业技术对中小规模农户而言仍然相对昂贵且技术复杂，尤其是在发展中国家。本文提出了一种基于GNSS的低成本精准农业监测系统，集成了ESP32、农业传感器、物联网通信和实时网页可视化。该系统采集土壤水分、温度、相对湿度和GNSS数据，并将带有位置标记的测量数据传输至网页服务器以实现远程监测。","2026-09-14T00:00:00Z",{"impact":27,"substance":174,"depth":498,"authority":87,"freshness":27,"relevant":8,"comment":3102},"面向中小农户的低成本GNSS+ESP32物联网监测方案，方法具体但属单篇应用型论文，影响范围有限，可作为智慧农业技术案例收录。",[3104],{"name":2932,"url":3097},[32,34,82,1208,3106],"GNSS",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3110},{"candidates":3111,"agricultural_evidence":51},[3112,3116,3120],{"code":62,"score":24,"supported":51,"title":3113,"abstract":3114,"tags":3115},[4],[34,1098,4],[34],{"code":78,"score":27,"supported":51,"title":3117,"abstract":3118,"tags":3119},[],[82,83],[32,82],{"code":197,"score":79,"supported":51,"title":3121,"abstract":3122,"tags":3123},[],[200,201],[1208],[3125,3078],"土壤墒情 智慧农业 精准农业 物联网","土壤墒情智慧农业精准农业物联网-2654","10.38124\u002Fijisrt\u002F26aug1576",{"doi":3127,"openalex_id":3129,"authors":3130,"venue":2932,"cited_by_count":119,"oa_url":3139,"card":3140,"direction":128,"ingested_from":130},"W7213355767",[3131,3133,3134,3135,3137],{"name":3132,"orcid":17},"D. Z. Zakut",{"name":2974,"orcid":17},{"name":2972,"orcid":17},{"name":3136,"orcid":17},"S. A. Yusuf",{"name":3138,"orcid":17},"I. Isa","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1576.pdf",{"tldr":3141,"method":3142,"finding":3143,"direction":128,"opportunity":3144},"设计并实现了一套低成本GNSS精准农业监测系统，集成ESP32与物联网实时可视化。","采用ESP32、农业传感器、GNSS模块与IoT通信，采集土壤水分、温湿度等数据","系统能以低成本实现位置标记的农田环境实时远程监测，适合中小农户。","可进一步研究低功耗长期部署、多源数据融合与面向小农户的智能预警决策模型。","2026-09-16T23:30:17.408536Z",{"id":3147,"title":3148,"url":3149,"summary":3150,"summary_zh":3151,"content":17,"source_name":3152,"source_url":3149,"published_at":2933,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":139,"score_detail":3153,"sources":3155,"tags":3157,"primary_topic":3160,"secondary_topics":3161,"classification":3164,"search_phrases":3183,"slug":3186,"view_count":406,"doi":3187,"paper":3188,"created_at":3209},2646,"IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0356383","Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.","水稻叶片病害（RLD）的早期和实时检测对农民而言是一项重大挑战，尤其是在难以获取先进技术的农村地区。传统的深度学习模型通常需要大量计算资源，使其难以在实际农业环境中常用的移动或边缘设备上部署。尽管ResNet50、VGG16和InceptionV3等模型可以达到较高精度，但其计算成本高昂，可能不太适合在智慧农业系统中实时部署。此外，许多现有研究依赖有限的数据集，这可能限制模型在多样化真实条件下的泛化能力。尽管迁移学习可以提升分类性能，但开发适合实时物联网部署的轻量级模型仍具有挑战性。为解决这些局限，我们通过整合自采集样本和Kaggle样本，构建了一个包含7092张水稻叶片图像的混合数据集，并提出了IoT-RiceMobileNet，这是一种基于MobileNetV2改进的轻量级模型，可有效集成到我们开发的物联网系统中。所提出的模型优于迁移学习和深度学习基线模型，在保持低计算复杂度的同时，实现了99.19%的测试准确率和99.20%的精确率。我们进一步通过在构建的RLD数据集和多源数据集上采用分层5折和10折交叉验证确认了模型稳定性，在构建的RLD数据集上分别达到98.04%和98.13%的平均准确率，在多源数据集上分别达到98.11%和98.58%的平均准确率。此外，该模型紧凑的规模和85.17 FPS的推理速度支持实时部署。最后，该模型被集成到一个支持物联网的移动端、网页端和云端推理框架中，展示了其在智慧农业中可扩展水稻病害检测的实际潜力。","PLoS ONE",{"impact":24,"substance":25,"depth":24,"authority":141,"freshness":142,"relevant":8,"comment":3154},"提出轻量级MobileNetV2水稻病害检测模型并集成物联网推理框架，实测精度与推理速度俱佳，方法新颖、数据规模可观，对边缘端智慧农业落地有参考价值。",[3156],{"name":3152,"url":3149},[32,33,34,3158,3159],"水稻病害","轻量化模型",{"code":1585,"slug":1586,"name":1587,"path":1588,"parent_code":1020},[3162,3163],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":3165},{"candidates":3166,"agricultural_evidence":51},[3167,3171,3175,3179],{"code":1585,"score":397,"supported":51,"title":3168,"abstract":3169,"tags":3170},[1586],[1587,1586],[3158],{"code":62,"score":397,"supported":51,"title":3172,"abstract":3173,"tags":3174},[4],[34,4],[34],{"code":50,"score":27,"supported":51,"title":3176,"abstract":3177,"tags":3178},[],[55,53],[33,3159],{"code":78,"score":79,"supported":51,"title":3180,"abstract":3181,"tags":3182},[],[32,461],[32],[3184,3185],"农业人工智能 轻量化模型 智慧农业 水稻病害","农业人工智能 轻量化模型","农业人工智能轻量化模型智慧农业水稻病害-2646","10.1371\u002Fjournal.pone.0356383",{"doi":3187,"openalex_id":3189,"authors":3190,"venue":3152,"cited_by_count":119,"oa_url":3149,"card":3204,"direction":128,"ingested_from":130},"W7213328321",[3191,3193,3195,3197,3199,3201],{"name":3192,"orcid":17},"Khawja Imran Masud",{"name":3194,"orcid":17},"Md Yasin Zihad",{"name":3196,"orcid":17},"Mehedi Hasan Shuvo",{"name":3198,"orcid":17},"Mst Raonik Jannat",{"name":3200,"orcid":17},"Jia Uddin",{"name":3202,"orcid":3203},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",{"tldr":3205,"method":3206,"finding":3207,"direction":128,"opportunity":3208},"提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。","改进MobileNetV2，融合自采与Kaggle共7092张图像，IoT端部署","测试准确率99.19%，推理速度85.17 FPS，适合边缘设备实时检测。","可探索多作物病害泛化、田间复杂光照下轻量模型鲁棒性及边缘联邦学习。","2026-09-16T23:30:12.197386Z",{"id":3211,"title":3212,"url":3213,"summary":3214,"summary_zh":3215,"content":17,"source_name":3216,"source_url":3213,"published_at":2933,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1008,"score_detail":3217,"sources":3219,"tags":3221,"primary_topic":3222,"secondary_topics":3223,"classification":3225,"search_phrases":3244,"slug":3247,"view_count":205,"doi":3248,"paper":3249,"created_at":3259},2645,"IOT AND WEATHER DATA-DRIVEN SMART IRRIGATION FOR WATER-EFFICIENT CROP PRODUCTION","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fjcise\u002F2\u002F3\u002F012","Agriculture is one of the major consumers of freshwater, and inefficient irrigation practices can lead to significant water wastage, reduced crop productivity, and increased cultivation costs. Conventional irrigation methods often depend on fixed schedules or manual decisions and may not adequately respond to changing soil and weather conditions. This study proposes an IoT and Weather Data-Driven Smart Irrigation System for Water-Efficient Crop Production that enables intelligent and automated irrigation based on real-time field conditions. The proposed system integrates Internet of Things (IoT) sensors such as soil-moisture, temperature, humidity, and water-level sensors with external weather information including rainfall probability and temperature forecasts. The collected data are transmitted to a central monitoring platform using an IoT-enabled controller. Based on soil moisture levels and prevailing or predicted weather conditions, the system determines the appropriate timing and duration of irrigation. When adequate soil moisture or sufficient rainfall is expected, irrigation is reduced or postponed, thereby avoiding unnecessary water consumption. The proposed system was developed and evaluated using selected agricultural crops under controlled field conditions. Its performance was assessed in terms of water consumption, irrigation efficiency, soil moisture management, crop growth, and system reliability. Experimental results demonstrate a water savings of up to 35% compared to conventional schedule-based irrigation, alongside an 18.2% improvement in soil moisture stability. The study demonstrates how the integration of IoT and weather data can support precision agriculture, sustainable water management, and improved agricultural productivity.","农业是淡水的主要消耗者之一，低效的灌溉方式可能导致严重的水资源浪费、作物生产力下降以及种植成本增加。传统灌溉方法通常依赖固定时间表或人工决策，可能无法充分应对不断变化的土壤和天气条件。本研究提出了一种基于物联网与天气数据的智能灌溉系统，用于节水型作物生产，该系统能够根据实时田间条件实现智能化和自动化灌溉。所提出的系统将物联网（IoT）传感器（如土壤湿度、温度、湿度和水位传感器）与外部天气信息（包括降雨概率和温度预报）相结合。采集的数据通过支持物联网的控制器传输至中央监控平台。系统根据土壤湿度水平以及当前或预测的天气条件，确定适当的灌溉时间和时长。当土壤湿度充足或预计有足够降雨时，灌溉会减少或推迟，从而避免不必要的水资源消耗。所提出的系统在受控田间条件下选用特定农作物进行了开发和评估。其性能从耗水量、灌溉效率、土壤湿度管理、作物生长和系统可靠性等方面进行了评估。实验结果表明，与传统基于时间表的灌溉方式相比，该系统可节水高达35%，同时土壤湿度稳定性提高了18.2%。该研究展示了物联网与天气数据的融合如何支持精准农业、可持续水资源管理以及农业生产力的提升。","RCHUB JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)",{"impact":174,"substance":616,"depth":26,"authority":251,"freshness":27,"relevant":8,"comment":3218},"该论文提出物联网与气象数据驱动的智能灌溉系统，实测节水35%、土壤湿度稳定性提升18.2%，方法新颖且数据可靠，对智慧农业节水管理有实质参考价值，值得进入每日精选。",[3220],{"name":3216,"url":3213},[32,34,503,82,1209],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[3224],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":3226},{"candidates":3227,"agricultural_evidence":51},[3228,3232,3236,3240],{"code":158,"score":1150,"supported":51,"title":3229,"abstract":3230,"tags":3231},[159],[186,187,159],[503,1209],{"code":62,"score":24,"supported":51,"title":3233,"abstract":3234,"tags":3235},[4],[34,65,4],[34],{"code":78,"score":27,"supported":51,"title":3237,"abstract":3238,"tags":3239},[],[82,83],[32,82],{"code":197,"score":87,"supported":51,"title":3241,"abstract":3242,"tags":3243},[],[200,201],[],[3245,3246],"智慧农业 智能灌溉 精准农业 节水农业","智慧农业 智能灌溉","智慧农业智能灌溉精准农业节水农业-2645","10.65725\u002Fjcise\u002F2\u002F3\u002F012",{"doi":3248,"openalex_id":3250,"authors":3251,"venue":3216,"cited_by_count":119,"oa_url":17,"card":3254,"direction":128,"ingested_from":130},"W7213308969",[3252],{"name":3253,"orcid":17},"M. Rathamani",{"tldr":3255,"method":3256,"finding":3257,"direction":128,"opportunity":3258},"提出物联网与天气数据驱动的智能灌溉系统，按实时土壤与气象条件自动决策灌溉。","集成土壤湿度、温湿度、水位传感器与降雨概率、温度预报，经物联网控制器决策。","相比定时灌溉节水达35%，土壤湿度稳定性提升18.2%。","可探索多源气象预报误差下的灌溉决策鲁棒性，及跨作物、跨区域的节水模型泛化。","2026-09-16T23:30:12.064334Z",{"id":3261,"title":3262,"url":3263,"summary":3264,"summary_zh":3265,"content":17,"source_name":3266,"source_url":3263,"published_at":3267,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":119,"score_detail":3268,"sources":3270,"tags":3272,"primary_topic":17,"secondary_topics":3276,"classification":3277,"search_phrases":3299,"slug":3302,"view_count":8,"doi":3303,"paper":3304,"created_at":3323},2418,"Artificial Intelligence-Driven Anti-Poaching Surveillance Systems: A Review of Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488549\u002Fijece-v13i8p103","The UN Environment Programme reports an average 69% decline in wildlife population since 1970, with around 4000 species illegally poached, trafficked, and traded globally. The World Wildlife Crime Report shows a significant population revival for certain species, but overall protection remains inadequate. The 5-step approach to understanding wildlife decline can improve methods. Emerging smart technologies, such as computer vision, IoT systems, and AI, can help manage wildlife populations and prevent poaching. Drones, bio-loggers, camera traps, and GPS collars provide real-time data for wildlife monitoring and tracking. Unmanned Aerial Vehicles (UAVs) have also shown promise in wildlife welfare. This article aims to analyze the decline of wildlife populations, study smart technologies for anti-poaching efforts, and evaluate emerging technology-driven solutions to traditional wildlife welfare challenges. Human-induced factors like hunting, poaching, pollution, and habitat loss have compromised biodiversity. Poaching has led to population reduction and ecological imbalance. Traditional conservation practices have failed to revive wildlife populations. Integrating smart technologies, IoT, AI, and predictive analysis can help prevent poaching and promote ecological balance.","联合国环境规划署报告称，自1970年以来，野生动物种群平均减少了69%，全球约有4000个物种遭到非法偷猎、贩运和交易。《世界野生动物犯罪报告》显示，某些物种种群显著恢复，但总体保护仍然不足。理解野生动物减少的5步方法可以改进相关措施。新兴智能技术，如计算机视觉、物联网系统和人工智能，有助于管理野生动物种群并防止偷猎。无人机、生物记录器、相机陷阱和GPS项圈为野生动物监测和追踪提供实时数据。无人驾驶飞行器（UAVs）在野生动物福利方面也展现出前景。本文旨在分析野生动物种群的减少，研究反偷猎的智能技术，并评估新兴技术驱动的解决方案以应对传统野生动物福利挑战。狩猎、偷猎、污染和栖息地丧失等人为因素破坏了生物多样性。偷猎导致种群减少和生态失衡。传统保护实践未能恢复野生动物种群。整合智能技术、物联网、人工智能和预测分析有助于防止偷猎并促进生态平衡。","International Journal of Electronics and Communication Engineering","2026-09-11T00:00:00Z",{"impact":119,"substance":119,"depth":119,"authority":119,"freshness":119,"relevant":119,"comment":3269},"主题为野生动物反盗猎智能监测，属生态保护领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[3271],{"name":3266,"url":3263},[56,34,3273,3274,3275],"无人机遥感","野生动物保护","反盗猎监测",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":3278},{"candidates":3279,"agricultural_evidence":22,"reason":347},[3280,3284,3291,3295],{"code":50,"score":397,"supported":51,"title":3281,"abstract":3282,"tags":3283},[58],[56,59],[56],{"code":3285,"score":79,"supported":51,"title":3286,"abstract":3287,"tags":3290},"H6",[],[3288,3289],"生物多样性","biodiversity",[3274],{"code":62,"score":79,"supported":51,"title":3292,"abstract":3293,"tags":3294},[],[34,4],[34],{"code":633,"score":8,"supported":22,"title":3296,"abstract":3297,"tags":3298},[],[],[3273],[3300,3301],"野生动物保护 反盗猎监测 无人机遥感 计算机视觉","野生动物保护 反盗猎监测","野生动物保护反盗猎监测无人机遥感计算机视觉-2418","10.14445\u002F23488549\u002Fijece-v13i8p103",{"doi":3303,"openalex_id":3305,"authors":3306,"venue":3266,"cited_by_count":119,"oa_url":3317,"card":3318,"direction":128,"ingested_from":130},"W7212319763",[3307,3309,3311,3313,3315],{"name":3308,"orcid":17},"Rohit Samkaria",{"name":3310,"orcid":17},"Rajesh Singh",{"name":3312,"orcid":17},"Anita Gehlot",{"name":3314,"orcid":17},"Charvi Joshi",{"name":3316,"orcid":17},"Rahul Mahala","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJECE\u002F2026\u002FVolume13-Issue8\u002FIJECE-V13I8P103.pdf",{"tldr":3319,"method":3320,"finding":3321,"direction":128,"opportunity":3322},"综述AI、物联网与无人机等智能技术在反盗猎监测中的进展、挑战与未来方向。","综述计算机视觉、IoT、无人机、相机陷阱、GPS项圈等实时监测技术。","传统保护措施失效，AI与IoT融合可有效预防盗猎并促进生态平衡。","可探索AI预测模型与多源传感数据融合在反盗猎实时预警中的落地验证。","2026-09-14T23:30:12.295479Z",{"id":3325,"title":3326,"url":3327,"summary":3328,"summary_zh":3329,"content":17,"source_name":3330,"source_url":3327,"published_at":3267,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2124,"score_detail":3331,"sources":3333,"tags":3335,"primary_topic":3337,"secondary_topics":3338,"classification":3340,"search_phrases":3376,"slug":3379,"view_count":205,"doi":3380,"paper":3381,"created_at":3406},2302,"AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315503187260909160504","Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.","引言 快速的气候变化、土壤退化以及不断变化的环境条件使农民难以进行作物选择。本研究提出了一种基于物联网（IoT）和人工智能（AI）的框架，利用当前土壤条件和未来天气预报来推荐适宜的作物。研究还识别了影响作物选择的关键环境因素。方法 设计了一个三层架构，包括数据采集、通信和数据处理模块。通过物联网传感器采集氮、磷、钾、pH值、温度和湿度的实时数据，并结合降雨量和历史农业数据。使用包含多个环境特征和22种作物类别的数据集进行模型开发。应用机器学习和深度学习方法进行分类和预测，包括随机森林（Random Forest）、XGBoost、K近邻（KNN）、支持向量机（SVM）、卷积神经网络（CNN）、决策树（DT）、深度神经网络（DNN）和长短期记忆网络（LSTM）。采用准确率、F1分数、MAE、RMSE和R²评估性能。同时进行帕累托分析以识别最具影响力的参数。结果 随机森林和CNN取得了最高的分类准确率99.54%，F1分数为0.995，XGBoost也表现强劲，准确率为99.32%。回归分析表明，集成模型优于线性模型。帕累托分析显示，降雨量、湿度和钾是作物推荐中影响最大的因素。在实时案例研究中，该框架推荐水稻为给定输入条件下最适宜的作物。讨论 结果表明，将物联网感知与基于人工智能的预测相结合，有助于在播种前进行主动的作物规划，并在气候变化条件下改善可持续农业决策。结论 所提出的框架有效结合了实时监测、预测分析和智能作物推荐，为可扩展的精准农业系统提供了实用基础。","The Open Agriculture Journal",{"impact":24,"substance":616,"depth":26,"authority":251,"freshness":27,"relevant":8,"comment":3332},"该论文提出IoT与AI融合的智能选种框架，多模型对比与实时案例验证充分，方法新颖且结论可靠，对智慧农业精准种植有较高参考价值。",[3334],{"name":3330,"url":3327},[32,33,34,82,3336],"作物推荐",{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},[3339],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":3341},{"candidates":3342,"agricultural_evidence":51},[3343,3347,3351,3355,3359,3363,3367,3371],{"code":50,"score":24,"supported":51,"title":3344,"abstract":3345,"tags":3346},[58],[55,170,171,53],[33],{"code":78,"score":174,"supported":51,"title":3348,"abstract":3349,"tags":3350},[461],[82,83],[32,82],{"code":62,"score":397,"supported":51,"title":3352,"abstract":3353,"tags":3354},[4],[34,4],[34],{"code":1585,"score":87,"supported":51,"title":3356,"abstract":3357,"tags":3358},[],[1587,1586],[],{"code":197,"score":87,"supported":51,"title":3360,"abstract":3361,"tags":3362},[],[200,201],[],{"code":190,"score":87,"supported":51,"title":3364,"abstract":3365,"tags":3366},[],[193,194],[],{"code":926,"score":205,"supported":51,"title":3368,"abstract":3369,"tags":3370},[],[929],[],{"code":2300,"score":205,"supported":51,"title":3372,"abstract":3373,"tags":3375},[],[3374],"agricultural data",[],[3377,3378],"农业人工智能 作物推荐 智慧农业 精准农业","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业精准农业-2302","10.2174\u002F0118743315503187260909160504",{"doi":3380,"openalex_id":3382,"authors":3383,"venue":3330,"cited_by_count":119,"oa_url":3327,"card":3401,"direction":128,"ingested_from":130},"W7212307638",[3384,3386,3389,3392,3395,3398],{"name":3385,"orcid":17},"Shreya Sriram",{"name":3387,"orcid":3388},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":3390,"orcid":3391},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":3393,"orcid":3394},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":3396,"orcid":3397},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":3399,"orcid":3400},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":3402,"method":3403,"finding":3404,"direction":128,"opportunity":3405},"提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。","三层IoT架构采集NPK、pH等数据，用RF、CNN等8种模型分类22种作物。","RF与CNN分类准确率达99.54%，降雨、湿度和钾是作物选择最关键因素。","可探索多源遥感与边缘计算融合，实现小农户低成本、可解释的实时作物推荐系统。","2026-09-13T23:30:09.745236Z",{"id":3408,"title":3409,"url":3410,"summary":3411,"summary_zh":3412,"content":17,"source_name":3413,"source_url":3410,"published_at":3267,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":3414,"sources":3416,"tags":3418,"primary_topic":3420,"secondary_topics":3421,"classification":3422,"search_phrases":3434,"slug":3437,"view_count":406,"doi":3438,"paper":3439,"created_at":3451},2298,"A systematic review of data handling and management techniques in edge computing for IoT","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09533-w","The proliferation of Internet of Things (IoT) devices and latency-sensitive applications has increased the need for edge computing, which complements existing cloud systems by enabling data processing closer to the source. By processing data closer to the source, edge computing reduces latency, bandwidth consumption, and response time. Organisations face major challenges in data management because edge environments use decentralised systems and limited resources, which require them to manage multiple data management tasks, including storage resources, data caching, data aggregation and data integrity verification. The resolution of these problems is necessary to create Edge-IoT systems that can operate at a large scale while maintaining trustworthy and secure operations. This paper presents a comprehensive and systematic review of recent advancements that occurred between 2022 and 2025 in data handling optimisation techniques for edge computing in IoT applications. The study employs a structured review methodology that meets Scopus and Web of Science standards to evaluate research studies that are divided into four main categories, which include data storage optimisation, data caching strategies, data aggregation mechanisms and data integrity assurance techniques. A total of 52 primary technical studies were selected for detailed comparative analysis, while additional secondary literature sources were used for theoretical background and contextual discussion. The study conducts a comprehensive comparative analysis, which assesses performance through latency and energy consumption, system scalability, security overhead and system performance under varying workloads. The review evaluates application-specific use cases, which include smart cities, healthcare, industrial IoT, autonomous vehicles and smart agriculture, to demonstrate the real-world significance of present solutions. The review identifies gaps in adaptive optimisation, heterogeneous device support, privacy preservation, and cross-layer coordination as critical research problems. The document describes existing challenges and future research paths that will help create advanced data processing systems for edge-enabled Internet of Things networks.","物联网（IoT）设备的激增以及对延迟敏感的应用需求推动了边缘计算的发展，边缘计算通过在更接近数据源的位置进行数据处理，对现有云系统形成补充。通过在更接近数据源的位置处理数据，边缘计算降低了延迟、带宽消耗和响应时间。由于边缘环境采用去中心化系统且资源有限，组织在数据管理方面面临重大挑战，需要管理多项数据管理任务，包括存储资源、数据缓存、数据聚合和数据完整性验证。解决这些问题对于构建能够大规模运行且保持可信和安全操作的边缘物联网系统至关重要。本文对2022年至2025年间物联网应用中边缘计算数据处理优化技术的最新进展进行了全面系统的综述。本研究采用符合Scopus和Web of Science标准的结构化综述方法，将研究文献分为四大类进行评估，包括数据存储优化、数据缓存策略、数据聚合机制和数据完整性保障技术。共选取52篇主要技术研究进行详细比较分析，同时使用其他二手文献来源提供理论背景和情境讨论。本研究进行了全面的比较分析，从延迟和能耗、系统可扩展性、安全开销以及不同工作负载下的系统性能等方面评估性能。综述评估了特定应用场景，包括智慧城市、医疗健康、工业物联网、自动驾驶汽车和智慧农业，以展示当前解决方案的实际意义。综述指出了自适应优化、异构设备支持、隐私保护和跨层协调方面的不足，将其列为关键研究问题。本文描述了现有挑战和未来研究方向，有助于为边缘赋能的物联网网络构建先进的数据处理系统。","Discover Applied Sciences",{"impact":251,"substance":252,"depth":26,"authority":141,"freshness":27,"relevant":8,"comment":3415},"系统综述边缘计算数据管理技术，含智慧农业等应用场景，对农业物联网部署有参考价值，但非农业专属研究，影响范围有限。",[3417],{"name":3413,"url":3410},[32,181,34,1824,3419],"数据管理",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3423},{"candidates":3424,"agricultural_evidence":51},[3425,3430],{"code":62,"score":3426,"supported":51,"title":3427,"abstract":3428,"tags":3429},28,[4,66],[34,181,65,4,66],[1824,34,181],{"code":78,"score":79,"supported":51,"title":3431,"abstract":3432,"tags":3433},[],[32,461],[32],[3435,3436],"农业传感器 数据管理 智慧农业 边缘计算","农业传感器 数据管理","农业传感器数据管理智慧农业边缘计算-2298","10.1007\u002Fs42452-026-09533-w",{"doi":3438,"openalex_id":3440,"authors":3441,"venue":3413,"cited_by_count":119,"oa_url":3410,"card":3446,"direction":128,"ingested_from":130},"W7212114265",[3442,3444],{"name":3443,"orcid":17},"Sourabh Natu",{"name":3445,"orcid":17},"Ankur Goyal",{"tldr":3447,"method":3448,"finding":3449,"direction":128,"opportunity":3450},"系统综述2022-2025年IoT边缘计算数据处理与管理优化技术，涵盖存储、缓存、聚合与完整性四类。","遵循Scopus\u002FWoS标准的结构化综述，比较分析52篇主要技术研究。","识别出自适应优化、异构设备支持、隐私保护与跨层协调四大关键研究空白。","可将边缘数据管理优化方法迁移至农业IoT场景，填补农业边缘计算中自适应与跨层协调的研究空白。","2026-09-13T23:30:09.503310Z",{"id":3453,"title":3454,"url":3455,"summary":3456,"summary_zh":17,"content":17,"source_name":3457,"source_url":17,"published_at":3458,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":3459,"score_detail":3460,"sources":3463,"tags":3465,"primary_topic":17,"secondary_topics":3468,"classification":3469,"search_phrases":3484,"slug":3487,"view_count":8,"doi":17,"paper":3488,"created_at":3496},2273,"Scientific Reports 基于区块链的可信食品供应链透明度框架研究","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63360-y","提出基于区块链的食品供应链可追溯框架,集成IPFS、IoT传感器和混合GM-PBFT共识机制,采用Hyperledger Fabric和以太坊测试网络实验评估,实现99.3%的可追溯精度,召回响应速度提升37%,数据篡改风险降低。","Nature Scientific Reports","2026-09-06T16:00:00Z",84,{"impact":25,"substance":3461,"depth":1150,"authority":498,"freshness":87,"relevant":8,"comment":3462},23,"该研究提出集成IPFS、IoT与混合共识机制的可信食品供应链框架，实验数据扎实、方法新颖，对农业信息化与食品安全溯源具有较高参考价值，值得进入每日精选。",[3464],{"name":3457,"url":3455},[32,34,3466,279,3467],"区块链","供应链溯源",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":3470},{"candidates":3471,"agricultural_evidence":22,"reason":347},[3472,3476,3480],{"code":62,"score":287,"supported":51,"title":3473,"abstract":3474,"tags":3475},[],[4],[34],{"code":276,"score":406,"supported":22,"title":3477,"abstract":3478,"tags":3479},[],[],[3467,279],{"code":78,"score":8,"supported":22,"title":3481,"abstract":3482,"tags":3483},[],[],[32],[3485,3486],"供应链溯源 智慧农业 食品安全 区块链","供应链溯源 智慧农业","供应链溯源智慧农业食品安全区块链-2273",{"doi":17,"openalex_id":17,"authors":3489,"venue":17,"cited_by_count":119,"oa_url":17,"card":3490,"direction":128,"ingested_from":3495},[],{"tldr":3491,"method":3492,"finding":3493,"direction":128,"opportunity":3494},"提出基于区块链的食品供应链可追溯框架，实现高精度追溯与快速召回。","集成IPFS、IoT传感器与混合GM-PBFT共识，基于Hyperledger ","可追溯精度达99.3%，召回响应速度提升37%，数据篡改风险降低。","可探索轻量级共识与跨链互操作，降低中小农户上链成本与能耗。","agent","2026-09-13T00:04:05.211463Z",{"id":3498,"title":3499,"url":3500,"summary":3501,"summary_zh":3502,"content":17,"source_name":3503,"source_url":3500,"published_at":3504,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1576,"score_detail":3505,"sources":3507,"tags":3509,"primary_topic":3512,"secondary_topics":3513,"classification":3514,"search_phrases":3537,"slug":3540,"view_count":79,"doi":3541,"paper":3542,"created_at":3557},2152,"STEM Education and AI Applications in Chemistry Teaching: From Pedagogical Ecosystems to the Self-Efficacy of Secondary School Teachers","https:\u002F\u002Fdoi.org\u002F10.12691\u002Fwjce-14-3-3","This study explores the integration of STEM education and artificial intelligence (AI) in chemistry teaching to foster a sustainable pedagogical ecosystem. Current secondary school practices reveal three critical operational gaps: a deficiency in interdisciplinary integration methodologies at the lower secondary level, intense high-stakes national examination pressures driving technological risk aversion at the upper secondary level, and pervasive administrative formalism in teacher professional development. To address these challenges, the research combines conceptual framework construction with an exemplary case study of a chemistry STEM project entitled \"Chemical Fertilizers and Smart Agriculture\". This project is structured across three progressive technical layers: direct soil pH and nutrient analysis using IoT sensors in field environments (Layer 1); the integration of large language models (such as ChatGPT) as personalized learning co-pilots alongside virtual laboratories to optimize learning trajectories (Layer 2); and the process-oriented evaluation of 21st-century competencies via a Digital Portfolio platform (Layer 3). Based on these empirical findings, the study proposes a bipolar set of breakthrough solutions: activating teachers' internal \"self-efficacy\" through digital self-directed learning, and reforming the \"transformative mission\" of teacher education institutions through the development of smart campus laboratories and professional learning networks. Finally, the study recommends institutionalizing Digital Portfolios parallel to traditional academic transcripts to alleviate high-stakes examination pressures and establish a genuine symbiotic connection between teacher education institutions and secondary schools in the AI era.","本研究探讨了STEM教育与人工智能（AI）在化学教学中的融合，以构建可持续的教学生态系统。当前中学实践暴露出三个关键的操作性缺口：初中阶段跨学科整合方法的缺失，高中阶段高利害全国性考试压力导致的技术风险规避，以及教师专业发展中普遍存在的行政形式主义。为应对这些挑战，本研究将概念框架构建与题为“化肥与智慧农业”的化学STEM项目案例研究相结合。该项目按三个递进的技术层次进行架构：在田间环境中利用物联网传感器进行直接的土壤pH和养分分析（第一层）；将大语言模型（如ChatGPT）作为个性化学习协 pilot，与虚拟实验室相结合以优化学习路径（第二层）；以及通过数字档案平台对21世纪能力进行过程性评价（第三层）。基于这些实证发现，本研究提出了一组两极突破性解决方案：通过数字化自主学习激活教师内在的“自我效能感”，以及通过建设智慧校园实验室和专业学习网络来变革教师教育机构的“转型使命”。最后，本研究建议将数字档案与传统学业成绩单并行制度化，以缓解高利害考试压力，并在AI时代建立教师教育机构与中学之间真正的共生联系。","World journal of chemical education","2026-09-09T00:00:00Z",{"impact":27,"substance":24,"depth":174,"authority":251,"freshness":27,"relevant":8,"comment":3506},"以“化肥与智慧农业”项目为案例，探讨AI与物联网在化学教学中的融合路径，对农业信息化人才培养与教师数字素养建设有参考价值，但属教育研究范畴，产业影响有限。",[3508],{"name":3503,"url":3500},[32,33,3510,34,3511],"数字素养","STEM教育",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3515},{"candidates":3516,"agricultural_evidence":51},[3517,3521,3525,3529,3533],{"code":78,"score":79,"supported":51,"title":3518,"abstract":3519,"tags":3520},[],[32,461],[32],{"code":62,"score":79,"supported":51,"title":3522,"abstract":3523,"tags":3524},[],[34,4],[34],{"code":197,"score":87,"supported":51,"title":3526,"abstract":3527,"tags":3528},[],[200,201],[],{"code":50,"score":287,"supported":51,"title":3530,"abstract":3531,"tags":3532},[],[58],[33],{"code":2300,"score":8,"supported":22,"title":3534,"abstract":3535,"tags":3536},[],[],[3510],[3538,3539],"农业人工智能 数字素养 智慧农业 物联网","农业人工智能 数字素养","农业人工智能数字素养智慧农业物联网-2152","10.12691\u002Fwjce-14-3-3",{"doi":3541,"openalex_id":3543,"authors":3544,"venue":3503,"cited_by_count":119,"oa_url":3551,"card":3552,"direction":128,"ingested_from":130},"W7212041762",[3545,3548],{"name":3546,"orcid":3547},"Cao Thi Van Giang","https:\u002F\u002Forcid.org\u002F0009-0003-3483-9496",{"name":3549,"orcid":3550},"Cao Cự Giác","https:\u002F\u002Forcid.org\u002F0000-0003-4804-9009","https:\u002F\u002Fpubs.sciepub.com\u002Fwjce\u002F14\u002F3\u002F3\u002Fwjce-14-3-3.pdf",{"tldr":3553,"method":3554,"finding":3555,"direction":238,"opportunity":3556},"构建化学STEM教学框架，以“化肥与智慧农业”案例融合AI与物联网，提升教师自我效能。","概念框架+案例研究，用IoT传感器、ChatGPT、虚拟实验室和数字档案袋。","提出激活教师自我效能与改革师范教育的双极方案，建议数字档案袋制度化。","可实证检验AI+IoT农业项目对教师自我效能与学生21世纪能力的长效影响。","2026-09-11T23:30:13.553449Z",{"id":3559,"title":3560,"url":3561,"summary":3562,"summary_zh":3563,"content":17,"source_name":816,"source_url":3561,"published_at":3564,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":7,"score_detail":3565,"sources":3567,"tags":3569,"primary_topic":3570,"secondary_topics":3571,"classification":3574,"search_phrases":3597,"slug":3600,"view_count":1203,"doi":3601,"paper":3602,"created_at":3618},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","2026-09-10T00:00:00Z",{"impact":24,"substance":252,"depth":26,"authority":251,"freshness":27,"relevant":8,"comment":3566},"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[3568],{"name":816,"url":3561},[32,33,34,503,82,1582],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},[3572,3573],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},{"status":164,"locked":22,"version":45,"method":46,"evidence":3575},{"candidates":3576,"agricultural_evidence":51},[3577,3581,3585,3589,3593],{"code":158,"score":24,"supported":51,"title":3578,"abstract":3579,"tags":3580},[159],[186,187,159],[503],{"code":62,"score":397,"supported":51,"title":3582,"abstract":3583,"tags":3584},[4],[34,4],[34],{"code":197,"score":498,"supported":51,"title":3586,"abstract":3587,"tags":3588},[201],[200,201],[],{"code":78,"score":27,"supported":51,"title":3590,"abstract":3591,"tags":3592},[],[82,83],[32,82],{"code":50,"score":287,"supported":51,"title":3594,"abstract":3595,"tags":3596},[],[58],[33],[3598,3599],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140","10.22214\u002Fijraset.2026.84727",{"doi":3601,"openalex_id":3603,"authors":3604,"venue":816,"cited_by_count":119,"oa_url":3561,"card":3613,"direction":128,"ingested_from":130},"W7212115545",[3605,3607,3609,3611],{"name":3606,"orcid":17},"Gowri M.",{"name":3608,"orcid":17},"Boomika M.",{"name":3610,"orcid":17},"S. Rakshana",{"name":3612,"orcid":17},"Rubali R.",{"tldr":3614,"method":3615,"finding":3616,"direction":128,"opportunity":3617},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z",{"id":3620,"title":3621,"url":3622,"summary":3623,"summary_zh":3624,"content":17,"source_name":3625,"source_url":3622,"published_at":3504,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2576,"score_detail":3626,"sources":3628,"tags":3630,"primary_topic":17,"secondary_topics":3634,"classification":3635,"search_phrases":3646,"slug":3649,"view_count":8,"doi":3650,"paper":3651,"created_at":3672},2123,"Determining blooming-phase dynamics through intra-day hive-weight analysis","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102559","This study analyses intra-day beehive weight dynamics across previously delimited blooming periods, using the remote temporal analysis of hive weight data to focus on weight variations throughout the day. The data were collected at the University of Córdoba’s experimental apiary using the Wbee remote monitoring system. Hive weights were recorded every 5 minutes over two consecutive beekeeping seasons from fifteen hives (six in 2016, nine in 2017). The analysis examined temporal intra-day weight-variation patterns associated with pre-bloom, bloom and post-bloom phases and related daily weight variation to environmental conditions and bee activity. Daily hive-weight amplitude differed significantly between blooming phases (Kruskal–Wallis 𝐻 = 1 8 5 . 6 0 6 , 𝑝 = 4 . 9 6 8 × 1 0 − 4 1 ), and was highest during the Bloom phase (0.76 ± 0.55 kg) compared with Post-bloom (0.50 ± 0.98 kg) and Pre-bloom (0.47 ± 0.26 kg), with the timing of daily maximum weight also differing significantly between phases (Kruskal–Wallis 𝐻 = 1 5 7 . 1 2 2 , 𝑝 = 7 . 6 1 1 × 1 0 − 3 5 ). These results demonstrate the value of hive weight as one key metric for precision beekeeping. The observed temporal patterns may contribute to the development of future monitoring frameworks for colony management and environmental assessment. The findings also highlight the potential utility of continuous hive-weight monitoring for studying colony responses to environmental variability, while the small sample size and single-apiary design of this exploratory study call for validation across additional sites and seasons.","本研究基于先前界定的开花期，分析蜂箱日内重量动态，利用蜂箱重量数据的远程时间序列分析，重点考察一天内的重量变化。数据通过Wbee远程监测系统在科尔多瓦大学实验蜂场采集。在连续两个养蜂季节中，对15个蜂箱（2016年6个，2017年9个）每5分钟记录一次蜂箱重量。分析考察了与花前期、开花期和花后期相关的时间日内重量变化模式，并将每日重量变化与环境条件和蜜蜂活动联系起来。蜂箱每日重量振幅在开花阶段之间差异显著（Kruskal–Wallis 𝐻 = 185.606，𝑝 = 4.968 × 10⁻⁴¹），且在开花期最高（0.76 ± 0.55 kg），高于花后期（0.50 ± 0.98 kg）和花前期（0.47 ± 0.26 kg）；每日最大重量的出现时间在不同阶段之间也差异显著（Kruskal–Wallis 𝐻 = 157.122，𝑝 = 7.611 × 10⁻³⁵）。这些结果表明，蜂箱重量是精准养蜂的一项关键指标。所观察到的时间模式可能有助于未来用于蜂群管理和环境评估的监测框架的开发。研究结果还凸显了连续蜂箱重量监测在研究蜂群对环境变异性响应方面的潜在效用，同时这项探索性研究样本量小且仅涉及单一蜂场，因此需要在更多地点和季节进行验证。","Smart Agricultural Technology",{"impact":251,"substance":252,"depth":174,"authority":141,"freshness":79,"relevant":8,"comment":3627},"基于蜂箱重量日内动态识别开花期的探索性研究，方法新颖、数据连续，但样本小、单站点，属细分领域进展。",[3629],{"name":3625,"url":3622},[32,34,3631,3632,3633],"精准养蜂","蜂群监测","授粉管理",[],{"status":330,"locked":22,"version":45,"method":46,"evidence":3636},{"candidates":3637,"agricultural_evidence":22,"reason":347},[3638,3642],{"code":78,"score":8,"supported":22,"title":3639,"abstract":3640,"tags":3641},[],[],[32],{"code":62,"score":8,"supported":22,"title":3643,"abstract":3644,"tags":3645},[],[],[34],[3647,3648],"授粉管理 智慧农业 精准养蜂 蜂群监测","授粉管理 智慧农业","授粉管理智慧农业精准养蜂蜂群监测-2123","10.1016\u002Fj.atech.2026.102559",{"doi":3650,"openalex_id":3652,"authors":3653,"venue":3625,"cited_by_count":119,"oa_url":3666,"card":3667,"direction":128,"ingested_from":130},"W7212032505",[3654,3657,3659,3662,3664],{"name":3655,"orcid":3656},"José Luis Ávila-Jiménez","https:\u002F\u002Forcid.org\u002F0000-0001-8006-8256",{"name":3658,"orcid":17},"Francisco J. Rodriguez-Lozano",{"name":3660,"orcid":3661},"Manuel Ortíz-López","https:\u002F\u002Forcid.org\u002F0000-0001-8312-1729",{"name":3663,"orcid":17},"Hector Martínez",{"name":3665,"orcid":17},"Jose M. Flores","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007847\u002Fpdf",{"tldr":3668,"method":3669,"finding":3670,"direction":128,"opportunity":3671},"通过日内蜂箱重量动态分析，识别开花前中后阶段的蜂群活动差异。","Wbee系统每5分钟监测15个蜂箱重量，跨两个养蜂季，用Kruskal-Wall","开花期日内重量振幅最大（0.76kg），且日最大重量出现时间在阶段间显著不同。","可扩展多站点多季节验证，并融合环境传感器与机器学习构建精准蜂群管理预警模型。","2026-09-11T23:30:03.883635Z",{"id":3674,"title":3675,"url":3676,"summary":3677,"summary_zh":3678,"content":17,"source_name":3679,"source_url":3676,"published_at":3564,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":3680,"score_detail":3681,"sources":3683,"tags":3685,"primary_topic":3688,"secondary_topics":3689,"classification":3690,"search_phrases":3709,"slug":3712,"view_count":8,"doi":3713,"paper":3714,"created_at":3735},2118,"IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112420","IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses。Computers and Electronics in Agriculture","物联网增强的温室烟粉虱发生预测与预警系统","Computers and Electronics in Agriculture",73,{"impact":174,"substance":24,"depth":26,"authority":498,"freshness":27,"relevant":8,"comment":3682},"核心期刊发表的温室白粉虱IoT预测预警研究，方法新颖且面向设施农业实际防控需求，具备专业参考价值。",[3684],{"name":3679,"url":3676},[32,33,3686,34,3687],"设施农业","病虫害预警",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3691},{"candidates":3692,"agricultural_evidence":51},[3693,3697,3701,3705],{"code":62,"score":397,"supported":51,"title":3694,"abstract":3695,"tags":3696},[4],[34,4],[34],{"code":464,"score":205,"supported":51,"title":3698,"abstract":3699,"tags":3700},[],[577],[],{"code":78,"score":8,"supported":22,"title":3702,"abstract":3703,"tags":3704},[],[],[32],{"code":50,"score":8,"supported":22,"title":3706,"abstract":3707,"tags":3708},[],[],[33],[3710,3711],"农业人工智能 病虫害预警 智慧农业 设施农业","农业人工智能 病虫害预警","农业人工智能病虫害预警智慧农业设施农业-2118","10.1016\u002Fj.compag.2026.112420",{"doi":3713,"openalex_id":3715,"authors":3716,"venue":3679,"cited_by_count":119,"oa_url":3676,"card":3730,"direction":128,"ingested_from":130},"W7212169080",[3717,3720,3723,3725,3727],{"name":3718,"orcid":3719},"Lin‐Ya Chiu","https:\u002F\u002Forcid.org\u002F0000-0003-1691-5582",{"name":3721,"orcid":3722},"Dan Jeric Arcega Rustia","https:\u002F\u002Forcid.org\u002F0000-0002-5855-8109",{"name":3724,"orcid":17},"Ya‐Fang Wu",{"name":3726,"orcid":17},"Jui‐Yung Chung",{"name":3728,"orcid":3729},"Ta‐Te Lin","https:\u002F\u002Forcid.org\u002F0000-0003-0852-1372",{"tldr":3731,"method":3732,"finding":3733,"direction":128,"opportunity":3734},"构建物联网增强的温室白粉虱发生预测预警系统。","物联网传感器采集温室环境数据，结合预测模型实现白粉虱预警。","物联网数据可提升温室白粉虱发生预测与早期预警能力。","可探索多模态数据融合与迁移学习，提升不同温室场景下虫害预警泛化性。","2026-09-11T23:30:01.850931Z",{"id":3737,"title":3738,"url":3739,"summary":3740,"summary_zh":17,"content":3741,"source_name":3742,"source_url":17,"published_at":3743,"category":2289,"cover_url":17,"hotness":21,"is_selected":22,"score":1517,"score_detail":3744,"sources":3746,"tags":3748,"primary_topic":17,"secondary_topics":3752,"classification":3753,"search_phrases":3770,"slug":3773,"view_count":406,"doi":17,"paper":17,"created_at":3774},2081,"山东临沭罗庄黄山镇\"低成本轻量化\"基层数字治理经验获山东省大数据局推介","https:\u002F\u002Fdsjj.linyi.gov.cn\u002Finfo\u002F1312\u002F6520.htm","山东临沂罗庄区黄山镇依托低成本物联改造、轻量化数字履职、全维度巡防感知,因地制宜探索基层数字治理模式,获评山东省大数据局2025年度公共视频智能应用示范案例。该镇布设55组智能接收终端完成应急广播数字化改造,为344户重点农户安装一氧化碳中毒智能报警器,搭建\"电力大数据\"监管模型、空天地一体感知网络,实现\"人力蹲守\"向\"智能预警\"转变。","[无障碍浏览](javascript:void(0);)\n\n*   [首页](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex.htm \"首页\")\n*   [机构职能](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fzfxxgk\u002Fjgzn.htm \"机构职能\")\n*   [政府信息公开](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fzfxxgk.htm \"政府信息公开\")\n*   [工作动态](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fgzdt.htm \"工作动态\")\n*   [机关党建](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fjgdj.htm \"机关党建\")\n*   [数据开放](http:\u002F\u002Flydata.sd.gov.cn\u002F \"数据开放\")\n*   [数字临沂](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fszly.htm \"数字临沂\")\n*   [服务大厅](http:\u002F\u002Flyzwfw.sd.gov.cn\u002Flys\u002Fdepartment\u002FdepartmentHall\u002FdepartmentHallInfo?id=371300DSJJ \"服务大厅\")\n\n当前位置： [首页](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex.htm)>>[数字临沂](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fszly.htm)>>正文\n\n罗庄区黄山镇：坚持低成本轻量化 探索基层数字治理新路径\n2026-09-07 14:45 临沂市大数据局网站\n\n近年来，罗庄区黄山镇依托低成本物联改造、轻量化数字履职、全维度巡防感知，因地制宜探索基层 数字 治理模式，走出一条“投入少、见效快、易推广”的数字乡村建设新路径。相关经验做法获评山东省大数据局2025年度公共视频智能应用示范案例，并在山东省委社会工作部简报刊发。\n\n**聚焦“普惠可及”，以“低成本改造”消除数字鸿沟**\n\n一是完成应急广播数字化改造。升级镇村老旧广播设施，布设55组智能接收终端，搭建全域覆盖、轻量运行的广播体系，提升政策宣讲等工作效率，有效破解农村信息传递“最后一米”难题。二是推广普及物联网感知终端。将重点风险纳入物联网监管体系，为46家重点企业加装智能烟感器，为344户重点农户安装 一氧化碳中毒 智能报警器，为31辆村级巡逻车加装GPS定位设备，实现精准巡逻防控、隐患实时预警、险情快速处置。\n\n**聚焦“务实管用”，以“轻量化应用”促进基层减负**\n\n一是打造“极简”工作模式。布设55套巡更设备，在防溺水巡河、森林防火巡山等工作场景构建低门槛、易操作、轻量化的智慧巡防工作体系，大幅简化工作流程，各重点区域监管到位率达99%。二是实现“一图”统管全局。创新应用“兰图绘”小程序，将391处限额以下工地、汛期转移安置点等关键信息“落图”管理，实现全周期可视化追溯。通过数据共享，国土卫片图斑数量常年保持全区最低，汛期转移用时户均提速15分钟，实现让数据跑路、让干部省心。\n\n**聚**焦“价值挖掘”，以“全维度感知”激活数据潜能****\n\n一是搭建“电力大数据”监管模型。归集全镇 816家加工经营单位用电数据，常态化分析生产动态，实现生产状况实时感知、违规作业智能预警，构建起“数据即线索、电力即探头”的无感监管新模式。二是构建“空天地一体”感知网络。推动智能化巡防力量全域下沉，搭建科技化巡查体系，将高空瞭望、智能监控、无人机航拍、雪亮工程 等数字化技术运用到秸秆禁烧、耕地保护、禁种铲毒、治安防控等重点领域，依靠科技手段精准识别隐患、快速排查问题、闭环高效处置。通过“电眼+天眼”互补，实现了从“人力蹲守”到“智能预警”的转变，大幅提升了风险防控的精准度。\n\n下一步，罗庄区黄山镇将继续深化基层 数字 治理，做强数据底座、拓展应用场景、输出治理经验，加快提升乡村治理精细化、智慧化、现代化水平。\n\n【[关闭窗口](javascript:window.opener=null;window.open('','_self');window.close();)】\n\n临沂市大数据局主办 临沂市网站集约化平台技术支持 网站标识码：3713000077\n\n联系电话：0539-8770501 0539-8770503\n\n[鲁ICP备2025159968号](http:\u002F\u002Fbeian.miit.gov.cn\u002F)![Image 1](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fimages\u002Fbeian.png)[鲁公网安备 37130202372003号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=37130202372003)[网站地图](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex\u002Fwzdt.htm)","临沂市大数据局","2026-09-07T00:00:00Z",{"impact":174,"substance":24,"depth":397,"authority":142,"freshness":87,"relevant":8,"comment":3745},"山东省级示范案例，低成本轻量化数字治理路径具体可复制，数据与场景细节扎实，对数字乡村建设有参考价值。",[3747],{"name":3742,"url":3739},[2296,34,3749,3750,3751],"基层治理","电力大数据","智能巡防",[],{"status":3754,"locked":22,"version":45,"method":46,"evidence":3755},"unclassified",{"candidates":3756,"agricultural_evidence":51,"reason":3769},[3757,3761,3765],{"code":2317,"score":8,"supported":22,"title":3758,"abstract":3759,"tags":3760},[],[],[3749],{"code":62,"score":8,"supported":22,"title":3762,"abstract":3763,"tags":3764},[],[],[34],{"code":2300,"score":8,"supported":22,"title":3766,"abstract":3767,"tags":3768},[],[],[2296],"缺少足够的主题证据",[3771,3772],"电力大数据 基层治理 数字乡村 智能巡防","电力大数据 基层治理","电力大数据基层治理数字乡村智能巡防-2081","2026-09-11T00:04:18.642881Z",{"id":3776,"title":3777,"url":3778,"summary":3779,"summary_zh":3780,"content":17,"source_name":3781,"source_url":3778,"published_at":3782,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":119,"score_detail":3783,"sources":3785,"tags":3787,"primary_topic":3788,"secondary_topics":3789,"classification":3790,"search_phrases":3809,"slug":3810,"view_count":8,"doi":3811,"paper":3812,"created_at":3829},1982,"A comprehensive survey on connectivity for IoT sensor networks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02296-6","Recent demand for Internet of things (IoT) based sensing devices has increased in many significant areas, such as greenhouse monitoring, remote patient monitoring, military applications, wild-life tracking and habitat monitoring, precision agriculture, etc. Moreover, some critical issues are present in the perpetual sensor network like node energy consumption rate, coverage\u002Fconnectivity, quality of service, network throughput and charging cycle capacity of sensor node batteries. Thereby, various network issues can arise, especially regarding coverage holes, proper connectivity, and network life-time. In this paper, we have studied these problems well and have shown the necessary constraints for the connectivity of the IoT sensor network. Moreover, we have also presented various helpful IoT applications and energy harvested IoT sensor networks. Any network can perform well for a long time because of energy management which is very crucial. This paper presents various feature scopes used in wireless sensor networks (WSNs). It also illustrates distinct approaches like relay node placement, efficient communication, machine-learning systems i.e. reinforcement learning, and heuristic techniques. It also discusses real-world deployments to highlight practical connectivity challenges and failures.","近年来，基于物联网（IoT）的传感设备需求在诸多重要领域显著增长，如温室监测、远程患者监护、军事应用、野生动物追踪与栖息地监测、精准农业等。此外，永久性传感器网络中仍存在一些关键问题，如节点能耗速率、覆盖\u002F连通性、服务质量、网络吞吐量以及传感器节点电池的充电周期容量。由此，可能引发多种网络问题，尤其是覆盖空洞、适当连通性和网络寿命方面。本文深入研究了这些问题，并展示了物联网传感器网络连通性所需的必要约束条件。同时，我们还介绍了多种有益的物联网应用及能量采集型物联网传感器网络。任何网络若要长期良好运行，都离不开能量管理，这一点至关重要。本文阐述了无线传感器网络（WSNs）中使用的多种功能范围，并展示了不同方法，如中继节点部署、高效通信、机器学习系统（即强化学习）以及启发式技术。此外，本文还讨论了实际部署案例，以突出实际中的连通性挑战与失败情况。","Peer-to-Peer Networking and Applications","2026-09-08T00:00:00Z",{"impact":119,"substance":119,"depth":119,"authority":119,"freshness":119,"relevant":119,"comment":3784},"该论文综述物联网传感器网络连接性，未涉及三农或农业信息化，不相关。",[3786],{"name":3781,"url":3778},[34],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3791},{"candidates":3792,"agricultural_evidence":51},[3793,3797,3801,3805],{"code":62,"score":24,"supported":51,"title":3794,"abstract":3795,"tags":3796},[4],[34,1838,65,4,1839],[34],{"code":464,"score":87,"supported":51,"title":3798,"abstract":3799,"tags":3800},[],[577,575],[],{"code":78,"score":87,"supported":51,"title":3802,"abstract":3803,"tags":3804},[],[82,83],[],{"code":50,"score":205,"supported":51,"title":3806,"abstract":3807,"tags":3808},[],[170],[],[],"AcomprehensivesurveyonconnectivityforIoT-1982","10.1007\u002Fs12083-026-02296-6",{"doi":3811,"openalex_id":3813,"authors":3814,"venue":3781,"cited_by_count":119,"oa_url":3823,"card":3824,"direction":128,"ingested_from":130},"W7211879551",[3815,3818,3820],{"name":3816,"orcid":3817},"Ramesh Kumar","https:\u002F\u002Forcid.org\u002F0000-0001-9436-905X",{"name":3819,"orcid":17},"Dipak Kumar Sah",{"name":3821,"orcid":3822},"Rati Bhan","https:\u002F\u002Forcid.org\u002F0000-0001-5600-5386","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12083-026-02296-6.pdf",{"tldr":3825,"method":3826,"finding":3827,"direction":997,"opportunity":3828},"综述物联网传感器网络连接性，涵盖应用、约束、能量管理及部署挑战。","综述WSNs中连接性约束、能量管理、中继节点部署、强化学习及启发式方法。","连接性受能量限制，需结合中继部署、机器学习与能量采集技术。","可研究农业场景下能量采集与连接性优化，如基于强化学习的节点部署策略。","2026-09-09T23:30:08.466137Z",{"id":3831,"title":3832,"url":3833,"summary":3834,"summary_zh":3835,"content":17,"source_name":3679,"source_url":3833,"published_at":3743,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":3836,"sources":3838,"tags":3840,"primary_topic":3843,"secondary_topics":3844,"classification":3846,"search_phrases":3861,"slug":3864,"view_count":8,"doi":3865,"paper":3866,"created_at":3891},1882,"Continuous nitrate and ammonium monitoring under different VPD conditions in hydroponic cultivation via IoT environmental control and ion sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112400","Continuous nitrate and ammonium monitoring under different VPD conditions in hydroponic cultivation via IoT environmental control and ion sensing。Computers and Electronics in Agriculture","在水培栽培中，通过物联网环境控制与离子传感技术，对不同蒸汽压差（VPD）条件下的硝酸盐和铵态氮进行连续监测。《农业计算机与电子》期刊。",{"impact":397,"substance":252,"depth":24,"authority":397,"freshness":406,"relevant":8,"comment":3837},"研究结合IoT与离子传感监测水培营养液，方法新颖，对精准农业有参考价值，但发表于一年前，时效性低。",[3839],{"name":3679,"url":3833},[32,34,446,3841,3842],"离子传感","环境调控",{"code":464,"slug":560,"name":561,"path":562,"parent_code":563},[3845],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":3847},{"candidates":3848,"agricultural_evidence":51},[3849,3853,3857],{"code":62,"score":397,"supported":51,"title":3850,"abstract":3851,"tags":3852},[4],[34,4],[34],{"code":464,"score":141,"supported":51,"title":3854,"abstract":3855,"tags":3856},[467],[467],[446,3842],{"code":78,"score":8,"supported":22,"title":3858,"abstract":3859,"tags":3860},[],[],[32],[3862,3863],"智慧农业 环境调控 离子传感 物联网","智慧农业 环境调控","智慧农业环境调控离子传感物联网-1882","10.1016\u002Fj.compag.2026.112400",{"doi":3865,"openalex_id":3867,"authors":3868,"venue":3679,"cited_by_count":119,"oa_url":17,"card":3886,"direction":128,"ingested_from":130},"W7211918988",[3869,3872,3874,3876,3878,3881,3883],{"name":3870,"orcid":3871},"Yi-Min Wu","https:\u002F\u002Forcid.org\u002F0009-0002-2612-0556",{"name":3873,"orcid":17},"Wei-Lun Kuo",{"name":3875,"orcid":17},"Shao-Yuan Liu",{"name":3877,"orcid":17},"Hsing-Ying Chung",{"name":3879,"orcid":3880},"Chi-Yi Lin","https:\u002F\u002Forcid.org\u002F0000-0002-5631-8806",{"name":3882,"orcid":17},"Wei Fang",{"name":3884,"orcid":3885},"Lin‐Chi Chen","https:\u002F\u002Forcid.org\u002F0000-0001-5947-2019",{"tldr":3887,"method":3888,"finding":3889,"direction":128,"opportunity":3890},"利用物联网环境控制和离子传感，在水培中连续监测不同VPD条件下的硝酸盐和铵浓度。","物联网环境控制结合离子传感技术，监测不同VPD下的营养液离子浓度。","VPD条件影响水培营养液中硝酸盐和铵的浓度变化，连续监测可揭示其动态。","可延伸研究VPD与其他离子（如钾、钙）的关联，或开发基于实时离子数据的营养液智能调控算法。","2026-09-08T23:30:01.608742Z",{"id":3893,"title":3894,"url":3895,"summary":3896,"summary_zh":3897,"content":17,"source_name":3898,"source_url":3895,"published_at":3899,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":249,"score_detail":3900,"sources":3902,"tags":3904,"primary_topic":3905,"secondary_topics":3906,"classification":3909,"search_phrases":3941,"slug":3944,"view_count":287,"doi":3945,"paper":3946,"created_at":3959},1724,"Integrating Artificial Intelligence and IoT for Precision Farming: A Review of Agriculture 4.0 Technologies, Applications, and Challenges","https:\u002F\u002Fdoi.org\u002F10.67564\u002Firassjms.v3.i9.0186","Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.","农业4.0标志着从机械化、化学密集型农业向数据驱动、自动化、互联的食品生产模式的决定性转变。这一转变的核心在于人工智能（AI）与物联网（IoT）的融合，二者共同使农场能够以人工方法无法企及的精度感知、解读并响应田间条件。本综述综合了当前关于精准农业中AI-IoT整合的文献体系，涵盖核心使能技术——智能传感器、无人机（UAVs）、地理信息系统（GIS）、边缘与云计算，以及基于区块链的可追溯性——以及它们组合成功能性智慧农业系统的分层架构。基于主要发表于2019年至2025年间的同行评审研究，本综述考察了智能灌溉、作物病害检测、产量预测和供应链可追溯性中的代表性应用，整合了文献中反复出现的障碍，并对十项代表性研究进行了按年份排序的技术、数据集、性能指标及报告局限性的详细比较。综述最后指出了研究空白——特别是围绕经济实惠的边缘AI模型、互操作性数据标准，以及在印度等小农情境下的区域特定验证——这些值得在未来工作中予以关注。","IRASS Journal of Multidisciplinary Studies","2026-09-04T00:00:00Z",{"impact":24,"substance":25,"depth":24,"authority":21,"freshness":406,"relevant":8,"comment":3901},"综述AI与IoT在精准农业中的集成应用，涵盖技术、应用与挑战，对行业有参考价值，但时效性较低。",[3903],{"name":3898,"url":3895},[32,33,34,82],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[3907,3908],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"code":708,"slug":1526,"name":1527,"path":1528,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":3910},{"candidates":3911,"agricultural_evidence":51},[3912,3916,3920,3924,3928,3932,3937],{"code":62,"score":24,"supported":51,"title":3913,"abstract":3914,"tags":3915},[4],[34,65,4],[34],{"code":50,"score":251,"supported":51,"title":3917,"abstract":3918,"tags":3919},[58],[58],[33],{"code":708,"score":142,"supported":51,"title":3921,"abstract":3922,"tags":3923},[],[711,712,1543],[],{"code":78,"score":27,"supported":51,"title":3925,"abstract":3926,"tags":3927},[],[32,82],[32,82],{"code":158,"score":87,"supported":51,"title":3929,"abstract":3930,"tags":3931},[],[186,159],[],{"code":2598,"score":205,"supported":51,"title":3933,"abstract":3934,"tags":3936},[],[3935],"机械化",[],{"code":1345,"score":205,"supported":51,"title":3938,"abstract":3939,"tags":3940},[],[1349],[],[3942,3943],"农业人工智能 智慧农业 精准农业 物联网","农业人工智能 智慧农业","农业人工智能智慧农业精准农业物联网-1724","10.67564\u002Firassjms.v3.i9.0186",{"doi":3945,"openalex_id":3947,"authors":3948,"venue":3898,"cited_by_count":119,"oa_url":3953,"card":3954,"direction":128,"ingested_from":130},"W7208758888",[3949,3951],{"name":3950,"orcid":17},"G Swetha",{"name":3952,"orcid":17},"Prof T Anuradha","https:\u002F\u002Firasspublisher.com\u002Fassets\u002Farticles\u002F1788493097.pdf",{"tldr":3955,"method":3956,"finding":3957,"direction":128,"opportunity":3958},"综述AI与IoT在精准农业中的集成技术、应用与挑战，提出未来研究方向。","系统综述2019-2025年文献，分析架构、应用案例及十项代表性研究。","AI-IoT集成提升农业效率，但存在成本、互操作性和小农验证等挑战。","研究空白包括低成本边缘AI模型、数据标准互操作及小农场景的区域验证，可探索适应性方案。","2026-09-05T23:30:09.434649Z",{"id":3961,"title":3962,"url":3963,"summary":3964,"summary_zh":3965,"content":17,"source_name":3966,"source_url":3963,"published_at":3899,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":551,"score_detail":3967,"sources":3969,"tags":3971,"primary_topic":3973,"secondary_topics":3974,"classification":3975,"search_phrases":3994,"slug":3996,"view_count":406,"doi":3997,"paper":3998,"created_at":4014},1723,"Bridging challenges with technology: A scientometric and systematic study on sustainability in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.59953\u002Fpaperasia.v42i4b.1489","The rapid evolution of agricultural practices driven by population growth, climate change, and technological advancements has necessitated the integration of smart technologies to enhance productivity and sustainability. This study aims to achieve two research objectives: (1) to provide a comprehensive overview of research patterns and key contributors in the domain of technology integration in smart agriculture, and (2) to evaluate the advantages of integrating technology in smart agriculture to address existing challenges. A scientometric analysis and a systematic literature review (SLR) were employed to examine the research landscape and synthesize current evidence. Following the PRISMA screening process, 18 articles were retained for the final systematic review. The findings reveal the transformative role of the Internet of Things (IoT), artificial intelligence (AI), blockchain, and unmanned aerial vehicles (UAVs) in improving resource management, reducing labour costs, enhancing real-time monitoring, and strengthening farm safety. The scientometric analysis further highlights global research collaborations, influential contributors, and emerging technological trends shaping the field of smart agriculture. These findings demonstrate the significant potential of advanced technologies to address contemporary agricultural challenges while promoting sustainable and resilient farming practices worldwide.","人口增长、气候变化和技术进步推动农业实践快速演变，这要求整合智能技术以提升生产力和可持续性。本研究旨在实现两个研究目标：（1）全面概述智能农业技术整合领域的研究模式及主要贡献者；（2）评估智能农业中技术整合的优势，以应对现有挑战。采用科学计量分析和系统文献综述（SLR）来考察研究格局并综合现有证据。经过PRISMA筛选流程，最终纳入18篇文章进行系统综述。研究结果揭示了物联网（IoT）、人工智能（AI）、区块链和无人机（UAVs）在改善资源管理、降低劳动力成本、增强实时监测及强化农场安全方面的变革性作用。科学计量分析进一步凸显了全球研究合作、有影响力的贡献者以及塑造智能农业领域的新兴技术趋势。这些发现表明，先进技术在应对当代农业挑战、同时促进全球可持续和韧性农业实践方面具有巨大潜力。","PaperAsia",{"impact":251,"substance":24,"depth":174,"authority":27,"freshness":406,"relevant":8,"comment":3968},"系统综述与科学计量分析，梳理智慧农业技术应用与挑战，但时效性较低。",[3970],{"name":3966,"url":3963},[32,3972,33,34,3466],"无人机",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":3976},{"candidates":3977,"agricultural_evidence":51},[3978,3982,3986,3990],{"code":78,"score":251,"supported":51,"title":3979,"abstract":3980,"tags":3981},[461],[461],[32],{"code":62,"score":21,"supported":51,"title":3983,"abstract":3984,"tags":3985},[],[34,65,4],[34],{"code":190,"score":87,"supported":51,"title":3987,"abstract":3988,"tags":3989},[],[193,194],[],{"code":50,"score":287,"supported":51,"title":3991,"abstract":3992,"tags":3993},[],[58],[33],[3995,3943],"农业人工智能 智慧农业 区块链 无人机","农业人工智能智慧农业区块链无人机-1723","10.59953\u002Fpaperasia.v42i4b.1489",{"doi":3997,"openalex_id":3999,"authors":4000,"venue":3966,"cited_by_count":119,"oa_url":3963,"card":4009,"direction":128,"ingested_from":130},"W7208757733",[4001,4003,4005,4007],{"name":4002,"orcid":17},"Nor Suzylah Sohaimi",{"name":4004,"orcid":17},"Nor Syahidah Ishak",{"name":4006,"orcid":17},"Rozaimi Majid",{"name":4008,"orcid":17},"Siti Noor Zilawati Mingat@Minhad",{"tldr":4010,"method":4011,"finding":4012,"direction":128,"opportunity":4013},"通过科学计量和系统综述，评估智能农业中技术整合的研究格局与优势。","科学计量分析结合PRISMA系统文献综述，筛选18篇文章。","IoT、AI、区块链和无人机提升资源管理、降低成本、增强监测与安全。","可深入探究区块链与AI在农业中的协同应用，或针对特定作物\u002F区域的实证研究。","2026-09-05T23:30:09.377361Z",{"id":4016,"title":4017,"url":4018,"summary":4019,"summary_zh":17,"content":17,"source_name":4020,"source_url":17,"published_at":4021,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1459,"score_detail":4022,"sources":4024,"tags":4026,"primary_topic":4027,"secondary_topics":4028,"classification":4029,"search_phrases":4052,"slug":4054,"view_count":205,"doi":17,"paper":4055,"created_at":4062},1504,"Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring","https:\u002F\u002Fwww.journaljsrr.com\u002Findex.php\u002FJSRR\u002Farticle\u002Fview\u002F4484","研究构建了集成物联网传感器、无人机、深度学习与 Web\u002F移动应用的农业 AI 系统，用于作物与水产健康监测。数据集包括 7 万张图像与 3 个月的物联网传感器数据，按 80:10:10 划分训练\u002F验证\u002F测试。模型结果：YOLOv8 害虫识别准确率 92.1%，CNN 病害识别准确率 93.4%，虾类识别 88.4%；整体系统准确率 97%，传感器预测 RMSE 为 1.2。结论指出 AI、物联网与无人机技术可有效检测生物与非生物胁迫，对可持续农业系统形成支撑。原文标题：Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring。","Journal of Scientific Research and Reports 32(9) 468-478","2026-09-02T00:00:00Z",{"impact":24,"substance":252,"depth":26,"authority":251,"freshness":406,"relevant":8,"comment":4023},"研究集成物联网、无人机与深度学习，实现作物和水产健康监测，数据详实，模型准确率高，对智慧农业有实质参考价值。",[4025],{"name":4020,"url":4018},[32,3972,33,34,750],{"code":38,"slug":39,"name":40,"path":41,"parent_code":17},[],{"status":44,"locked":22,"version":45,"method":46,"evidence":4030},{"candidates":4031,"agricultural_evidence":51,"reason":114},[4032,4036,4040,4044,4048],{"code":50,"score":397,"supported":51,"title":4033,"abstract":4034,"tags":4035},[58],[55,58],[33],{"code":62,"score":397,"supported":51,"title":4037,"abstract":4038,"tags":4039},[65],[34,65],[34],{"code":772,"score":287,"supported":51,"title":4041,"abstract":4042,"tags":4043},[],[775],[750],{"code":926,"score":205,"supported":51,"title":4045,"abstract":4046,"tags":4047},[],[929],[],{"code":78,"score":8,"supported":22,"title":4049,"abstract":4050,"tags":4051},[],[],[32],[4053,3943],"农业人工智能 智慧农业 水产养殖 无人机","农业人工智能智慧农业水产养殖无人机-1504",{"doi":17,"openalex_id":17,"authors":4056,"venue":17,"cited_by_count":119,"oa_url":17,"card":4057,"direction":128,"ingested_from":3495},[],{"tldr":4058,"method":4059,"finding":4060,"direction":128,"opportunity":4061},"构建集成物联网、无人机与深度学习的农业AI系统，实现作物与水产健康监测。","集成物联网传感器、无人机、YOLOv8与CNN，使用7万图像及3个月传感器数据训","系统准确率97%，害虫识别92.1%，病害93.4%，虾类88.4%，传感器预测RMSE1.2。","可探索多源数据融合与边缘计算，提升实时监测精度，并扩展至更多水产种类与复杂环境。","2026-09-03T00:06:44.772165Z",{"id":4064,"title":4065,"url":4066,"summary":4067,"summary_zh":4068,"content":17,"source_name":816,"source_url":4066,"published_at":4069,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":4070,"score_detail":4071,"sources":4073,"tags":4075,"primary_topic":4076,"secondary_topics":4077,"classification":4078,"search_phrases":4093,"slug":4095,"view_count":1203,"doi":4096,"paper":4097,"created_at":4107},1419,"Smart Agriculture with Quantum Intelligence and Blockchain Security","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84736","Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamperresistant clustering for precision agriculture","在农业无线传感器与物联网（IoT）网络中，选择节能且可信的簇头（CH）仍是关键瓶颈，诸如LEACH等启发式协议往往收敛于不稳定配置，且无法验证所选节点是否可信。本文提出一个框架，将量子启发遗传算法（QIGA）用于簇头选择，并融合许可型区块链信任验证层。与以往将量子分类器应用于传感器数据分类的研究不同，本方法将候选簇头分配编码为量子比特染色体，并通过量子旋转门更新进行演化，将簇形成视为组合优化问题而非分类任务。验证后的分配结果与节点信誉评分被记录为不可篡改的区块链交易，并通过轻量级拜占庭容错共识机制加以验证，从而在无中心权威的情况下自动排除低信任节点。将所提框架与LEACH及经典遗传算法基线进行离散事件仿真对比，结果表明，在不同攻击比率下，该框架在能量保持方面表现相当或更优，并能可靠检测恶意节点。研究结果表明，将量子启发组合优化与区块链验证信任相结合，为实现精准农业中能量感知、防篡改的分簇提供了一条实用且不依赖硬件的路径。","2026-08-31T00:00:00Z",59,{"impact":251,"substance":24,"depth":174,"authority":21,"freshness":205,"relevant":8,"comment":4072},"提出量子启发遗传算法与区块链结合的簇头选择框架，方法新颖，但尚处仿真阶段，影响有限。",[4074],{"name":816,"url":4066},[32,33,34,3466],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":4079},{"candidates":4080,"agricultural_evidence":51},[4081,4085,4089],{"code":78,"score":397,"supported":51,"title":4082,"abstract":4083,"tags":4084},[461],[82,83],[32],{"code":62,"score":21,"supported":51,"title":4086,"abstract":4087,"tags":4088},[],[34,1838,65,4,1839],[34],{"code":50,"score":8,"supported":22,"title":4090,"abstract":4091,"tags":4092},[],[],[33],[4094,3943],"农业人工智能 智慧农业 区块链 物联网","农业人工智能智慧农业区块链物联网-1419","10.22214\u002Fijraset.2026.84736",{"doi":4096,"openalex_id":4098,"authors":4099,"venue":816,"cited_by_count":119,"oa_url":4066,"card":4102,"direction":128,"ingested_from":130},"W7204875790",[4100],{"name":4101,"orcid":17},"Munta Padmavathi",{"tldr":4103,"method":4104,"finding":4105,"direction":997,"opportunity":4106},"提出量子启发遗传算法与区块链结合，用于农业物联网中可信且节能的簇头选择。","量子启发遗传算法（QIGA）优化簇头选择，结合许可区块链和拜占庭容错共识验证信任","相比LEACH和经典遗传算法，QIGA+区块链在能量保持和恶意节点检测上相当或更优。","可探索将量子启发优化与区块链用于动态作物监测场景，或扩展到边缘计算与真实硬件部署验证。","2026-09-02T23:30:17.512521Z",{"id":4109,"title":4110,"url":4111,"summary":4112,"summary_zh":4113,"content":17,"source_name":2932,"source_url":4111,"published_at":4021,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":551,"score_detail":4114,"sources":4116,"tags":4118,"primary_topic":4120,"secondary_topics":4121,"classification":4123,"search_phrases":4158,"slug":4161,"view_count":406,"doi":4162,"paper":4163,"created_at":4179},1398,"AI-Enabled IoT for Precision Agriculture and Smart Crop Management","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1195","Agriculture is increasingly dependent on technologies that can improve productivity while reducing the consumption of water, fertilizers, pesticides, herbicides, labour and other resources. The five source chapters reviewed for this paper collectively describe the role of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), computer vision, cloud computing and embedded systems in smart farming. The summarized work focuses on continuous sensing of soil and environmental parameters, automated irrigation, crop and weed monitoring, disease identification, yield prediction and decision support. The sources identify Artificial Neural Networks (ANNs), Deep Learning, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as important approaches. A prototype architecture is also described using Arduino Mega 2560, Raspberry Pi, multiple sensors, Firebase and an Apache web server. In the disease-detection prototype, 295 leaf images were divided into training, validation and testing groups, and a CNN-based mobile application produced a reported confidence score of 0.97 for an example operation with an execution time of approximately 0.88 seconds. Overall, the reviewed material indicates that integrating IoT sensing with AI\u002FML can support real-time monitoring, resource optimization and faster agricultural decisions. However, Internet dependence, cybersecurity, system complexity, adoption cost, limited datasets and reduced accuracy for visually similar crop diseases remain important challenges.","农业日益依赖于能够在提高生产力的同时减少水、肥料、农药、除草剂、劳动力及其他资源消耗的技术。本文所综述的五篇源文献共同阐述了人工智能（AI）、机器学习（ML）、物联网（IoT）、计算机视觉、云计算及嵌入式系统在智慧农业中的作用。这些研究工作的重点集中于土壤与环境参数的连续感知、自动化灌溉、作物与杂草监测、病害识别、产量预测及决策支持。相关文献将人工神经网络（ANNs）、深度学习、支持向量机（SVMs）和卷积神经网络（CNNs）视为重要技术方法。此外，文中还描述了一种基于Arduino Mega 2560、Raspberry Pi、多种传感器、Firebase及Apache Web服务器的原型架构。在病害检测原型中，295张叶片图像被划分为训练集、验证集和测试集，基于CNN的移动应用在示例操作中报告的置信度得分为0.97，执行时间约为0.88秒。总体而言，所综述的材料表明，将物联网感知与人工智能\u002F机器学习相结合，可支持实时监测、资源优化及更快速的农业决策。然而，互联网依赖、网络安全、系统复杂性、采用成本、数据集有限以及视觉上相似的作物病害识别精度降低等问题仍是重要挑战。",{"impact":251,"substance":24,"depth":174,"authority":27,"freshness":406,"relevant":8,"comment":4115},"综述AI与IoT在精准农业中的应用，含原型与数据，但来源一般且时效性低。",[4117],{"name":2932,"url":4111},[32,33,34,57,4119],"作物监测",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[4122],{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":4124},{"candidates":4125,"agricultural_evidence":51},[4126,4130,4134,4138,4142,4146,4150,4154],{"code":62,"score":24,"supported":51,"title":4127,"abstract":4128,"tags":4129},[4],[34,65,4],[34],{"code":78,"score":251,"supported":51,"title":4131,"abstract":4132,"tags":4133},[83],[32],[32],{"code":50,"score":835,"supported":51,"title":4135,"abstract":4136,"tags":4137},[],[55,170,56,171,53,58,59,340],[33,57],{"code":69,"score":87,"supported":51,"title":4139,"abstract":4140,"tags":4141},[],[72,73],[],{"code":197,"score":87,"supported":51,"title":4143,"abstract":4144,"tags":4145},[],[200,201],[],{"code":158,"score":87,"supported":51,"title":4147,"abstract":4148,"tags":4149},[],[186,159],[],{"code":211,"score":205,"supported":51,"title":4151,"abstract":4152,"tags":4153},[],[910],[],{"code":633,"score":8,"supported":22,"title":4155,"abstract":4156,"tags":4157},[],[],[4119],[4159,4160],"农业人工智能 病虫害识别 作物监测 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物监测智慧农业-1398","10.38124\u002Fijisrt\u002F26aug1195",{"doi":4162,"openalex_id":4164,"authors":4165,"venue":2932,"cited_by_count":119,"oa_url":4111,"card":4174,"direction":128,"ingested_from":130},"W7204992523",[4166,4168,4170,4172],{"name":4167,"orcid":17},"Tarun Badiwal",{"name":4169,"orcid":17},"Manish Jain",{"name":4171,"orcid":17},"Sandeep Jayswal",{"name":4173,"orcid":17},"Suresh Meena",{"tldr":4175,"method":4176,"finding":4177,"direction":128,"opportunity":4178},"综述AI与IoT在精准农业中的应用，涵盖传感、灌溉、病害检测等，并给出原型架构。","综述多篇文献，结合ANN、DL、SVM、CNN，并描述基于Arduino和树莓派","AI与IoT集成可支持实时监测和资源优化，但存在网络依赖、成本高、数据有限等挑战。","针对视觉相似作物病害识别精度不足，可研究多模态数据融合或轻量化模型以提升鲁棒性。","2026-09-02T23:30:09.623547Z",{"id":4181,"title":4182,"url":4183,"summary":4184,"summary_zh":17,"content":17,"source_name":4185,"source_url":17,"published_at":4186,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2290,"score_detail":4187,"sources":4189,"tags":4191,"primary_topic":4193,"secondary_topics":4194,"classification":4195,"search_phrases":4210,"slug":4213,"view_count":406,"doi":17,"paper":4214,"created_at":4221},1370,"基于 I2C 多路复用的智能植物工厂微气候监测传感网络","https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F9\u002F366","巴西坎皮纳斯大学团队基于 ESP32 微控制器与 TCA9548A I2C 多路复用器，构建低成本、多路复用的植物工厂微气候物联网监测网络，可在 PFAL 结构内不同高度同步运行 5 个 SHT20 温湿度传感器；实验证明多路复用架构生成相干的多点测量；但依赖本地 Wi-Fi 网络，断网时出现间歇性连接故障和数据丢失；提出本地数据缓冲或专用私有网络等补充策略。","AgriEngineering · MDPI · 2026-09-01","2026-08-31T16:00:00Z",{"impact":251,"substance":24,"depth":174,"authority":251,"freshness":79,"relevant":8,"comment":4188},"基于ESP32与I2C多路复用构建低成本植物工厂监测网络，方法新颖，数据可靠，但依赖Wi-Fi的局限值得关注。",[4190],{"name":4185,"url":4183},[32,34,4192,2070],"植物工厂",{"code":464,"slug":560,"name":561,"path":562,"parent_code":563},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":4196},{"candidates":4197,"agricultural_evidence":51},[4198,4202,4206],{"code":464,"score":251,"supported":51,"title":4199,"abstract":4200,"tags":4201},[4192],[4192],[4192],{"code":62,"score":287,"supported":51,"title":4203,"abstract":4204,"tags":4205},[],[34],[34],{"code":78,"score":8,"supported":22,"title":4207,"abstract":4208,"tags":4209},[],[],[32],[4211,4212],"智慧农业 植物工厂 环境监测 物联网","智慧农业 植物工厂","智慧农业植物工厂环境监测物联网-1370",{"doi":17,"openalex_id":17,"authors":4215,"venue":17,"cited_by_count":119,"oa_url":17,"card":4216,"direction":128,"ingested_from":3495},[],{"tldr":4217,"method":4218,"finding":4219,"direction":128,"opportunity":4220},"构建基于ESP32和I2C多路复用的低成本植物工厂微气候监测网络，实现多点温湿度同步测量。","ESP32微控制器、TCA9548A I2C多路复用器、SHT20传感器，构建多","多路复用架构能生成相干的多点测量，但依赖Wi-Fi，断网时数据丢失。","可研究断网情况下的本地数据缓冲或边缘计算策略，提升植物工厂监测网络的鲁棒性。","2026-09-02T00:05:07.192287Z",{"id":4223,"title":4224,"url":4225,"summary":4226,"summary_zh":4227,"content":17,"source_name":4228,"source_url":4225,"published_at":4069,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2064,"score_detail":4229,"sources":4231,"tags":4233,"primary_topic":4234,"secondary_topics":4235,"classification":4241,"search_phrases":4288,"slug":4291,"view_count":205,"doi":4292,"paper":4293,"created_at":4305},1305,"IoT-Based Soil Condition Monitoring System for Supporting Precision and Sustainable Agriculture in West Java","https:\u002F\u002Fdoi.org\u002F10.55981\u002Fjet.807","West Java Province is one of the largest horticultural producers in Indonesia. The horticulture subsector in West Java covers tens of thousands of hectares of farmland and contributes approximately 1.8% to the province’s total Gross Regional Domestic Product (GRDP). However, this sector faces serious challenges. Climate change has increased the frequency of droughts and floods, disrupting horticultural production. In addition, soil fertility has declined due to unsustainable farming practices; many agricultural lands in West Java contain less than 2% organic matter as a result of long-term chemical fertilizer use. These challenges have led to decreased productivity and threaten the sustainability of horticultural farming. Precision agriculture emerges as a solution to these issues. This concept leverages IoT technologies such as the ESP32 microcontroller with SPIFFS, soil sensors, and a web-based dashboard to monitor land conditions in real time and manage inputs more efficiently. The IoT-based soil monitoring system developed in this study was tested in a farmer’s yard in Pameungpeuk Subdistrict, Garut. The results demonstrated practical benefits, including improved irrigation efficiency (up to ~30% water savings) and reduced risk of crop losses through early detection of drought conditions. Moreover, the application of IoT in potato cultivation in West Java has been shown to double seed tuber yields from 15,000 to 30,000 tubers. These findings are consistent with regional policy directions, as reflected in the West Java Regional Medium-Term Development Plan (RPJMD) and the “Petani Milenial” (Millennial Farmers) program, which promote sustainable agricultural modernization through IoT-based technologies to enhance the productivity of young farmers.","西爪哇省是印度尼西亚最大的园艺生产地之一。该省的园艺分部门覆盖数万公顷农田，约占全省地区生产总值（GRDP）总额的1.8%。然而，该部门面临严峻挑战。气候变化增加了干旱和洪水的发生频率，扰乱了园艺生产。此外，由于不可持续的耕作方式，土壤肥力下降；长期使用化肥导致西爪哇许多农业用地的有机质含量低于2%。这些挑战导致生产力下降，并威胁到园艺农业的可持续性。精准农业应运而生，成为解决这些问题的方案。这一概念利用物联网（IoT）技术，如搭载SPIFFS的ESP32微控制器、土壤传感器和基于网页的仪表盘，实时监测土地状况并更高效地管理投入。本研究中开发的基于物联网的土壤监测系统在加鲁特县帕梅翁佩乌克分区的一位农民庭院中进行了测试。结果表明其具有实际效益，包括提高灌溉效率（节水约30%）以及通过早期检测干旱状况降低作物损失风险。此外，物联网在西爪哇马铃薯种植中的应用已被证明可使种薯产量翻倍，从每公顷15,000个增加到30,000个。这些发现与区域政策方向一致，正如西爪哇省中期发展规划（RPJMD）和“千禧农民”计划所反映的那样，这些政策通过基于物联网的技术促进可持续农业现代化，以提高年轻农民的生产力。","Jurnal Elektronika dan Telekomunikasi",{"impact":397,"substance":24,"depth":174,"authority":21,"freshness":406,"relevant":8,"comment":4230},"论文展示IoT土壤监测系统在西爪哇的应用，节水30%并提升马铃薯产量，对区域精准农业有参考价值，但时效性较低。",[4232],{"name":4228,"url":4225},[32,34,82,929,1582],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[4236,4237],{"code":197,"slug":201,"name":261,"path":262,"parent_code":162},{"code":926,"slug":4238,"name":4239,"path":4240,"parent_code":162},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":164,"locked":22,"version":45,"method":46,"evidence":4242},{"candidates":4243,"agricultural_evidence":51},[4244,4248,4252,4257,4262,4266,4273,4277,4284],{"code":62,"score":397,"supported":51,"title":4245,"abstract":4246,"tags":4247},[4],[34,4],[34],{"code":197,"score":498,"supported":51,"title":4249,"abstract":4250,"tags":4251},[201],[200,201],[],{"code":926,"score":251,"supported":51,"title":4253,"abstract":4255,"tags":4256},[4254],"sustainable agriculture",[929],[929],{"code":190,"score":142,"supported":51,"title":4258,"abstract":4259,"tags":4261},[],[193,2838,194,4260],"drought",[],{"code":78,"score":27,"supported":51,"title":4263,"abstract":4264,"tags":4265},[],[82,83],[32,82],{"code":4267,"score":87,"supported":51,"title":4268,"abstract":4269,"tags":4272},"A5",[],[4270,4271],"马铃薯","potato",[],{"code":158,"score":87,"supported":51,"title":4274,"abstract":4275,"tags":4276},[],[186,159],[],{"code":4278,"score":87,"supported":51,"title":4279,"abstract":4280,"tags":4283},"J2",[],[4281,4282],"农业现代化","agricultural modernization",[],{"code":211,"score":205,"supported":51,"title":4285,"abstract":4286,"tags":4287},[],[911],[],[4289,4290],"可持续农业 土壤监测 智慧农业 精准农业","可持续农业 土壤监测","可持续农业土壤监测智慧农业精准农业-1305","10.55981\u002Fjet.807",{"doi":4292,"openalex_id":4294,"authors":4295,"venue":4228,"cited_by_count":119,"oa_url":4225,"card":4300,"direction":128,"ingested_from":130},"W7204810936",[4296,4298],{"name":4297,"orcid":17},"Ayu Latifah",{"name":4299,"orcid":17},"Dendi Ardimansah",{"tldr":4301,"method":4302,"finding":4303,"direction":128,"opportunity":4304},"开发基于IoT的土壤监测系统，支持西爪哇精准可持续农业，提高灌溉效率并增产。","使用ESP32微控制器、SPIFFS、土壤传感器和Web仪表盘构建实时监测系统。","系统节省约30%灌溉用水，马铃薯种薯产量翻倍至3万块，符合区域政策。","可扩展至更多作物和地区，结合机器学习预测土壤养分，优化施肥策略，提升系统普适性。","2026-09-01T23:30:12.797772Z",{"id":4307,"title":4308,"url":4309,"summary":4310,"summary_zh":17,"content":17,"source_name":4311,"source_url":4309,"published_at":4312,"category":20,"cover_url":17,"hotness":1644,"is_selected":22,"score":1008,"score_detail":4313,"sources":4315,"tags":4324,"primary_topic":4325,"secondary_topics":4326,"classification":4329,"search_phrases":4356,"slug":4358,"view_count":287,"doi":4359,"paper":4360,"created_at":4375},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",{"impact":24,"substance":25,"depth":24,"authority":141,"freshness":205,"relevant":8,"comment":4314},"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[4316,4317,4319,4321],{"name":4311,"url":4309},{"name":1006,"url":4318},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":1006,"url":4320},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":4322,"url":4323},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[32,33,181,34,503],{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[4327,4328],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},{"code":50,"slug":149,"name":150,"path":151,"parent_code":38},{"status":164,"locked":22,"version":45,"method":46,"evidence":4330},{"candidates":4331,"agricultural_evidence":51},[4332,4336,4340,4344,4348,4352],{"code":62,"score":2190,"supported":51,"title":4333,"abstract":4334,"tags":4335},[4,66],[65,4,66],[34,181],{"code":158,"score":251,"supported":51,"title":4337,"abstract":4338,"tags":4339},[159],[159],[503],{"code":50,"score":251,"supported":51,"title":4341,"abstract":4342,"tags":4343},[171],[171],[33],{"code":1105,"score":205,"supported":51,"title":4345,"abstract":4346,"tags":4347},[],[1109],[],{"code":197,"score":205,"supported":51,"title":4349,"abstract":4350,"tags":4351},[],[201],[],{"code":78,"score":8,"supported":22,"title":4353,"abstract":4354,"tags":4355},[],[],[32],[4357,3943],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":4359,"openalex_id":4361,"authors":4362,"venue":4311,"cited_by_count":119,"oa_url":4369,"card":4370,"direction":128,"ingested_from":130},"W7204189894",[4363,4366],{"name":4364,"orcid":4365},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":4367,"orcid":4368},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":4371,"method":4372,"finding":4373,"direction":128,"opportunity":4374},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":4377,"title":4378,"url":4379,"summary":4380,"summary_zh":17,"content":4381,"source_name":4382,"source_url":17,"published_at":4312,"category":4383,"cover_url":17,"hotness":21,"is_selected":51,"score":4384,"score_detail":4385,"sources":4388,"tags":4390,"primary_topic":4398,"secondary_topics":4399,"classification":4400,"search_phrases":4433,"slug":4436,"view_count":79,"doi":17,"paper":17,"created_at":4437},975,"农业法修订草案二审稿8月25日提请全国人大常委会审议 拟增加鼓励物联网、人工智能等新技术与农业生产经营管理相融合条款","http:\u002F\u002Fwww.npc.gov.cn\u002Fnpc\u002Fc2\u002Fc30834\u002F202608\u002Ft20260825_456878.html","新华社北京8月25日电：农业法修订草案二审稿25日提请十四届全国人大常委会第二十四次会议审议。草案二审稿完善了支持农业和农村经济发展的措施，增加统筹发展科技农业、绿色农业、质量农业、品牌农业，维护种业安全，提升种业现代化水平，提高农业保险保障水平和服务质量，鼓励『物联网、人工智能』等新技术与农业生产、经营、管理相融合等规定。同时加强乡村建设和治理，推进平安乡村建设。草案落实整治形式主义为基层减负要求，并规定国家支持开展农业领域国际交流合作。全国政协委员、中国社会科学院农村发展研究所杜志雄研究员建议在总则部分增加农业安全协同保障制度的规定。","新华社北京8月25日电（记者胡璐、韩佳诺）农业法修订草案二审稿25日提请十四届全国人大常委会第二十四次会议审议。草案二审稿拟完善支持农业和农村经济发展措施。\n\n农业法修订草案今年4月首次提请全国人大常委会会议审议，会后征求社会公众意见。经征求多方意见建议，形成了此次提请审议的草案二审稿。\n\n有的常委会组成人员和地方、部门建议，完善支持农业和农村经济发展的措施。对此，草案二审稿增加规定，包括统筹发展科技农业、绿色农业、质量农业、品牌农业；维护种业安全，提升种业现代化水平；提高农业保险保障水平和服务质量；鼓励物联网、人工智能等新技术与农业生产、经营、管理相融合。\n\n加强乡村建设和治理，对农业和农村经济发展至关重要。草案二审稿增加规定，国家加强乡村治理，推进平安乡村建设，构建和谐有序的乡村社会，不断增强广大农民的获得感、幸福感、安全感。\n\n为落实整治形式主义为基层减负的要求，草案二审稿增加各级人民政府应当建立健全整治形式主义为基层减负长效机制的规定。此外，草案二审稿还规定国家坚持平等互利、合作共赢的方针，支持开展农业领域的国际交流与合作，积极参与农业可持续发展与粮食安全国际治理。","中国人大网 2026-08-25 · 2026-08-25","政策",92,{"impact":3426,"substance":4386,"depth":26,"authority":397,"freshness":27,"relevant":8,"comment":4387},24,"全国性农业法修订草案，明确鼓励物联网、人工智能与农业融合，政策导向性强，信息权威且时效性佳。",[4389],{"name":4382,"url":4379},[32,34,4391,4392,4393,4394,4395,4396,4397],"人工智能","农业法修订草案","二审","农业","十四届人大常委会","种业安全","法治",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":4401},{"candidates":4402,"agricultural_evidence":51},[4403,4407,4413,4419,4425,4429],{"code":62,"score":251,"supported":51,"title":4404,"abstract":4405,"tags":4406},[34],[34],[34],{"code":4408,"score":205,"supported":51,"title":4409,"abstract":4410,"tags":4412},"F1",[],[4411],"种业",[],{"code":4414,"score":205,"supported":51,"title":4415,"abstract":4416,"tags":4418},"G",[],[4417],"农村发展",[],{"code":4420,"score":205,"supported":51,"title":4421,"abstract":4422,"tags":4424},"G3",[],[4423],"农业保险",[],{"code":78,"score":8,"supported":22,"title":4426,"abstract":4427,"tags":4428},[],[],[32],{"code":50,"score":8,"supported":22,"title":4430,"abstract":4431,"tags":4432},[],[],[4391],[4434,4435],"十四届人大常委会 农业法修订草案 人工智能 智慧农业","十四届人大常委会 农业法修订草案","十四届人大常委会农业法修订草案人工智能智慧农业-975","2026-08-27T00:05:17.098088Z",{"id":4439,"title":4440,"url":4441,"summary":4442,"summary_zh":17,"content":4443,"source_name":4444,"source_url":17,"published_at":4445,"category":2289,"cover_url":17,"hotness":21,"is_selected":51,"score":3459,"score_detail":4446,"sources":4448,"tags":4450,"primary_topic":4454,"secondary_topics":4455,"classification":4456,"search_phrases":4477,"slug":4480,"view_count":835,"doi":17,"paper":17,"created_at":4481},477,"设施农业鼓起农民\"钱袋子\"","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_7226a7cff5843652","国务院《加快农业农村现代化\"十五五\"规划》明确构建多元化食物供给体系、深入实施设施农业现代化提升行动。设施农业突破时空与资源限制,加速物联网、大数据、人工智能技术集成,延伸产业链价值链。甘肃建成全球最大集中连片日光温室生产区,河北设施果蔬产业成为京津\"菜篮子\"压舱石。文章提出从政策、科技、产业维度协同发力、加强核心技术研发与运用。","国务院印发的《加快农业农村现代化“十五五”规划》，明确了构建多元化食物供给体系，提出深入实施设施农业现代化提升行动。设施农业的独特优势，可以有效弥补我国地少人多、水资源短缺的农业生产短板，对于构建多元化食物供给体系、形成农业新质生产力具有重要战略意义。具体来讲，主要体现在三方面。\n\n一是突破时空与资源限制。设施农业有助于从根本上改变传统农业“靠天吃饭”的局面，它通过创新装备设施改造农业生产环境，使农业生产场域拓展到戈壁、盐碱地、丘陵、海洋甚至楼宇，既有效缓解了“粮菜争地”的矛盾，更在确保18亿亩耕地红线不动摇的前提下，开辟保障食物安全的“第二战场”。例如，甘肃省以戈壁农业闻名，充分利用戈壁滩丰富的太阳能资源，建成全球最大的集中连片日光温室生产区，使得戈壁设施农业面积达到空前规模。\n\n二是加速技术集成与创新应用。设施农业促使物联网、大数据、人工智能等前沿技术与农业生产实现深度融合，使农业从能够自动调控水肥、光照、温度和湿度的智慧温室，到垂直布局、无土栽培的植物工厂，再到能够精准饲喂的集约化养殖场，进而推动生产要素创新性配置，实现劳动生产率、土地产出率和资源利用率的大幅提升。\n\n三是延伸产业链与价值链。设施农业的推广加速了上下游产业协同并进。上游的种苗繁育、装备制造、新型农资等，中游的标准化生产等，下游的精深加工、冷链物流、品牌营销、休闲观光等，共同构成庞大产业集群，极大地提升农产品附加值，为农民提供了更多元的就业和增收渠道。比如，河北省的设施农业不仅是保障京津“菜篮子”的“压舱石”，更是富民兴村的支柱产业，有的地区通过发展高附加值设施果蔬产业，有效带动当地数万名农民“钱袋子”鼓起来，推动乡村全面振兴落地见效。\n\n目前，我国设施农业的发展仍存在总体规模不足、区域发展不平衡、技术装备水平参差不齐等问题。面向未来，应从政策、科技、产业等维度协同发力。\n\n加强各类政策支持。在严守耕地红线和生态保护红线的前提下，不断完善与设施农业相关的用地政策，持续优化用地分类管理和审批备案制度，保障设施农业发展的合理空间。优化财政补贴和金融配套服务，针对不同水平的设施、技术以及节能环保效益等调整补贴资金，引导更多社会资本在这一重点领域投资。\n\n加强核心技术研发与运用。不断完善产、学、研、用深度融合的技术创新体系，着力攻克农业机器人、高端传感器等“卡脖子”技术，大力推进数字技术与农艺深度融合。因地制宜加快建设能够融合数据采集、智能决策和精准作业的智慧农业示范区，促使更多创新成果转化为现实生产力。\n\n加强产业体系与企业品牌建设。引导设施农业从较多集中于生产环节向构建完整的现代产业体系转变，以推进标准化生产健全从田头到餐桌的全过程质量安全追溯体系。孵化一批具有高影响力的企业品牌，通过品牌溢价提升市场竞争力，鼓励这些企业以农产品深加工、休闲采摘、科普教育等实现价值链的持续攀升。\n\n加强经营主体的多元化和专业化。积极培育家庭农场、农民合作社等新型农业经营主体，探索发展多种形式的规模经营。提升对“新农人”的专业培训，打造更多爱农业、懂技术、善经营的专业化人才队伍，为设施农业发展提供优质人才保障。（作者：吕耀龙 来源：经济日报）","农民日报 2026-08-13","2026-08-12T16:00:00Z",{"impact":2338,"substance":252,"depth":24,"authority":251,"freshness":142,"relevant":8,"comment":4447},"全国性设施农业政策解读，内容详实，来源权威，时效性好，具有较高行业价值。",[4449],{"name":4444,"url":4441},[4451,3686,34,4452,4453],"十五五","日光温室","菜篮子",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":4457},{"candidates":4458,"agricultural_evidence":51},[4459,4464,4468,4472],{"code":4278,"score":87,"supported":51,"title":4460,"abstract":4461,"tags":4463},[],[4281,4462],"农业农村现代化",[],{"code":62,"score":287,"supported":51,"title":4465,"abstract":4466,"tags":4467},[],[34],[34],{"code":464,"score":205,"supported":51,"title":4469,"abstract":4470,"tags":4471},[],[577],[],{"code":4473,"score":8,"supported":22,"title":4474,"abstract":4475,"tags":4476},"J1",[],[],[4451],[4478,4479],"日光温室 设施农业 十五五 物联网","日光温室 设施农业","日光温室设施农业十五五物联网-477","2026-08-13T23:53:43.993976Z",{"id":4483,"title":4484,"url":4485,"summary":4486,"summary_zh":17,"content":4487,"source_name":4488,"source_url":17,"published_at":4489,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2879,"score_detail":4490,"sources":4492,"tags":4494,"primary_topic":4499,"secondary_topics":4500,"classification":4501,"search_phrases":4521,"slug":4524,"view_count":27,"doi":17,"paper":17,"created_at":4525},244,"Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816","arXiv 2026年8月4日上线的预印本,提出一种面向互联精准农场的AI驱动云边架构。研究针对番茄早疫病、晚疫病、叶霉病等重大病害导致严重减产的问题,集成物联网传感器、无人机、深度学习、Azure IoT Hub云端分析与多平台(移动应用、Web应用、嵌入式边缘设备平台)接口,实现番茄病害实时检测。模型基于PlantVillage、Kaggle等公开数据集训练,通过TensorFlow模型部署至移动、Web与边缘设备平台。实验结果显示检测有效性达92-95%,在不同环境与设备平台表现一致,显著降低人工巡检依赖,实现及时干预,支撑可持续互联精准农场发展。","[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816#content)\n\n![Image 1](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ficons\u002Fsmileybones-small.svg)System maintenance August 4th and 5th[Learn more](https:\u002F\u002Fstatus.arxiv.org\u002F)×\n\n[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 2: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Robotics\n\n**arXiv:2608.03816** (cs) \n\n [Submitted on 4 Aug 2026]\n\n# Title:Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms\n\nAuthors:[Deckshitha Angadi](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Angadi,+D), [Koteshwar Goud Surga](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Surga,+K+G), [Nagaraju Lakkaraju](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Lakkaraju,+N), [Naveena Budda](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Budda,+N), [Vikas Agarwal](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Agarwal,+V), [Giridhara Venkata Ram Raj Mulasa](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Mulasa,+G+V+R+R), [Ravi Killamsetty](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Killamsetty,+R), [Chandrasekhara Sarma Mallubhotla](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Mallubhotla,+C+S), [Narsimlu Kemsaram](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Kemsaram,+N)\n\nView a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.03816)\n> Abstract:Plant diseases cause significant yield losses worldwide, with tomato crops particularly susceptible to early blight, late blight, and leaf mold. Manual monitoring is practical only for small-scale farms and becomes unmanageable at larger scales. To tackle this limitation, an artificial intelligence (AI) enabled cloud-edge architecture is proposed for autonomous crop monitoring. This proposed architecture integrates Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), deep learning, Azure IoT Hub-based cloud analytics, and multi-platform (mobile app, web app, and embedded edge device platform) interfaces to enable real-time detection of tomato diseases. For training and validation, we used publicly available datasets, such as PlantVillage and Kaggle. A TensorFlow model trained on a collected dataset is deployed across mobile, web, and edge-device platforms. Experimental results show detection effectiveness around 92-95%, with consistent performance over diverse environments and device platforms. The proposed system improves disease detection effectiveness, lowers dependence on manual inspection, and enables prompt interventions, thereby supporting sustainable, connected precision agriculture farms.\n\nComments:Accepted for presentation at the 2026 IEEE International Conference on Sustainable AI for Social Impact and Global Development (SASIGD 2026), Hyderabad, India, 13-14 August 2026\nSubjects:Robotics (cs.RO)\nCite as:[arXiv:2608.03816](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816) [cs.RO]\n(or [arXiv:2608.03816v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816v1) [cs.RO] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.03816](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.03816)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\n From: Narsimlu Kemsaram Dr [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Fccb32526\u002F2608.03816)] \n\n**[v1]** Tue, 4 Aug 2026 15:25:02 UTC (16,022 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.03816)Full-text links:\n## Access Paper:\n\n View a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors\n\n*   [View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.03816)\n*   [TeX Source](https:\u002F\u002Farxiv.org\u002Fsrc\u002F2608.03816)\n\n[view license](http:\u002F\u002Farxiv.org\u002Flicenses\u002Fnonexclusive-distrib\u002F1.0\u002F \"Rights to this article\")\n\n### Current browse context:\n\ncs.RO\n\n[\u003Cprev](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2608.03816&function=prev&context=cs.RO \"previous in cs.RO (accesskey p)\") | [next>](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2608.03816&function=next&context=cs.RO \"next in cs.RO (accesskey n)\")\n\n[new](https:\u002F\u002Farxiv.org\u002Flist\u002Fcs.RO\u002Fnew) | 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\n\n[About](https:\u002F\u002Finfo.arxiv.org\u002Fabout)·[Help](https:\u002F\u002Finfo.arxiv.org\u002Fhelp)·[Contact](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fcontact.html)·[Subscribe](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fsubscribe)·[Copyright](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Flicense\u002Findex.html)·[Privacy](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fpolicies\u002Fprivacy_policy.html)·[Accessibility](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fweb_accessibility.html)·[Operational Status (opens in new tab)](https:\u002F\u002Fstatus.arxiv.org\u002F)\n\nMajor funding support from\n\n[![Image 5: Simons Foundation](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation.png)](https:\u002F\u002Fwww.simonsfoundation.org\u002F)[![Image 6: Simons Foundation International](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation-international.png)](https:\u002F\u002Fwww.sfi.org.bm\u002F)[![Image 7: Schmidt Sciences](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fschmidt-sciences.png)](https:\u002F\u002Fwww.schmidtsciences.org\u002F)","arXiv 2608.03816 (cs.RO) 2026-08-04","2026-08-04T00:00:00Z",{"impact":24,"substance":252,"depth":24,"authority":251,"freshness":142,"relevant":8,"comment":4491},"论文提出AI云边架构用于精准农业番茄病害检测，方法新颖，数据可靠，具有产业应用价值。",[4493],{"name":4488,"url":4485},[4495,34,4496,55,82,4497,4498],"预印本","arXiv","云边协同","番茄病害",{"code":62,"slug":154,"name":155,"path":156,"parent_code":38},[],{"status":164,"locked":22,"version":45,"method":46,"evidence":4502},{"candidates":4503,"agricultural_evidence":51},[4504,4508,4512,4517],{"code":78,"score":142,"supported":51,"title":4505,"abstract":4506,"tags":4507},[83],[],[82],{"code":62,"score":79,"supported":51,"title":4509,"abstract":4510,"tags":4511},[],[34,4],[34],{"code":108,"score":287,"supported":51,"title":4513,"abstract":4514,"tags":4516},[],[4515],"番茄",[4498],{"code":50,"score":287,"supported":51,"title":4518,"abstract":4519,"tags":4520},[],[55],[55],[4522,4523],"云边协同 深度学习 番茄病害 精准农业","云边协同 深度学习","云边协同深度学习番茄病害精准农业-244","2026-08-05T23:53:02.401550Z",{"id":2401,"title":4527,"url":4528,"summary":4529,"summary_zh":17,"content":4530,"source_name":4531,"source_url":17,"published_at":4532,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2124,"score_detail":4533,"sources":4535,"tags":4537,"primary_topic":17,"secondary_topics":4540,"classification":4541,"search_phrases":4561,"slug":4564,"view_count":21,"doi":4565,"paper":17,"created_at":4566},"基于边缘计算、机器学习与物联网的智能灌溉系统提升农业水资源管理（土耳其Mustafa & Kutucu，Frontiers 2026-07-28接受）","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull","Mustafa MS（卡拉比克大学计算机工程系）与Kutucu H（土耳其）在Frontiers in Sustainable Food Systems发表的原创研究\"Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT\"于2026-07-28接受。针对农业占全球淡水取用量约70%、传统灌溉水耗大、效率低的现实，研究构建将物联网、机器学习与边缘计算融合的智能灌溉系统：物联网传感器和边缘设备实时采集环境数据并估算灌溉需求，机器学习模型与土壤监测单元（SMU）和水管理单元（WMU）协同，在边缘本地部署实现可扩展、实时、高效灌溉管理。研究比较决策树（DT）、随机森林（RF）、支持向量机（SVM）、K近邻（KNN）、随机梯度下降（SGD）和多层感知机（MLP）六种监督学习模型，其中决策树和随机森林分类准确率达93%、R²回归得分0.98，显著优于SVM（准确率58%、R²=0.18），为不同农业场景下水资源高效管理提供可复用方案。原文：Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT","[](https:\u002F\u002Fwww.frontiersin.org\u002F)\n\n[![Image 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull) Frontiers in Sustainable Food Systems](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n About us\n\nAbout us\n\n*   Who we are\n*   [Mission and values](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fmission)\n*   [History](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fhistory)\n*   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Sustain. Food Syst.\n\nSec. Water-Smart Food Production\n\nVolume 10 - 2026 |  doi: 10.3389\u002Ffsufs.2026.1827416\n\n[Published in ![Image 6: Frontiers in Sustainable Food Systems](https:\u002F\u002Fd2csxpduxe849s.cloudfront.net\u002Fmedia\u002FE32629C6-9347-4F84-81FEAEF7BFA342B3\u002FF3B1CE16-45F3-4D96-BE72BE0491F0DAF2\u002Fwebimage-08DFEB67-EB21-43D0-995D8A1DFF53DB18.png) Frontiers in Sustainable Food Systems * #### Water-Smart Food Production * **4.1** impact factor * **6** citescore](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)[Part of a Research Topic Advanced technologies for water management: targeting sustainable agriculture Submission open * **19k** views * **11** articles](https:\u002F\u002Fwww.frontiersin.org\u002Fresearch-topics\u002F72689\u002Fadvanced-technologies-for-water-management-targeting-sustainable-agriculture)\n\n### Reviewers\n\nReviewed by\n\n![Image 7: 2 Anonymous reviewers](https:\u002F\u002Fstatic2.frontiersin.org\u002Fstatic-resources\u002Fimages\u002Fdefault_profileimage.webp)\n\n2 Anonymous reviewers\n\nOutline\n\n*    [Abstract](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull#h1)\n\n## ORIGINAL RESEARCH article\n\nFront. Sustain. Food Syst.\n\nSec. Water-Smart Food Production\n\n# Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT\n\n*   [![Image 8: Mohammed Sulaiman Mustafa](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3415048\u002F74) Mohammed Sulaiman Mustafa 1](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3415048)\n*   [![Image 9: Hakan Kutucu](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3429124\u002F74) Hakan Kutucu 2](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3429124)\n\n*   1. Karabuk Universitesi Bilgisayar Muhendislik Bolumu, Karabük, Türkiye\n\n*   2. Karabuk Universitesi Yazilim Muhendisligi Bolumu, Karabük, Türkiye\n\nArticle metrics\n\n[View details](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull#metrics)\n\n15\n\nViews\n\nThe final, formatted version of the article will be published soon.\n\nNotify me\n\nSelect one of your emails\n\nYou have multiple emails registered with Frontiers:\n\nNotify me\n\nNotify me on publication\n\nPlease enter your email address:\n\nEmail \n\nIf you already have an account, please [login](https:\u002F\u002Fwww.frontiersin.org\u002Flogin?returnUrl=\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fabstract)\n\nYou don't have a Frontiers account ? 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