[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2123":3,"related-2123":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":50,"slug":53,"view_count":22,"doi":54,"paper":55,"created_at":79},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。",null,"Smart Agricultural Technology","2026-09-09T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,13,7,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","物联网","精准养蜂","蜂群监测","授粉管理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":49},[39,44],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"I1",[],[],[27],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I5",[],[],[28],"农业相关性需要复核",[51,52],"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":54,"openalex_id":56,"authors":57,"venue":10,"cited_by_count":70,"oa_url":71,"card":72,"direction":76,"ingested_from":78},"W7212032505",[58,61,63,66,68],{"name":59,"orcid":60},"José Luis Ávila-Jiménez","https:\u002F\u002Forcid.org\u002F0000-0001-8006-8256",{"name":62,"orcid":9},"Francisco J. Rodriguez-Lozano",{"name":64,"orcid":65},"Manuel Ortíz-López","https:\u002F\u002Forcid.org\u002F0000-0001-8312-1729",{"name":67,"orcid":9},"Hector Martínez",{"name":69,"orcid":9},"Jose M. Flores",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007847\u002Fpdf",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-11T23:30:03.883635Z",{"total":81,"page":22,"page_size":81,"items":82},6,[83,191,298,369,431,487],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":109,"classification":110,"search_phrases":177,"slug":180,"view_count":70,"doi":181,"paper":182,"created_at":190},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。","International Journal of Drug Delivery Technology","2026-10-21T00:00:00Z",75,{"impact":93,"substance":94,"depth":95,"authority":96,"freshness":13,"relevant":22,"comment":97},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。",[99],{"name":89,"url":86},[27,101,28,102,103],"农业人工智能","杂草识别","病虫害监测",{"code":105,"slug":106,"name":107,"path":108,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":111,"locked":14,"version":35,"method":36,"evidence":112},"partial",{"candidates":113,"agricultural_evidence":116,"reason":176},[114,126,133,142,148,155,162,169],{"code":115,"score":93,"supported":116,"title":117,"abstract":119,"tags":125},"I2",true,[118],"deep learning",[120,121,122,118,123,124],"深度学习","计算机视觉","病虫害识别","artificial intelligence","computer vision",[101],{"code":45,"score":93,"supported":116,"title":127,"abstract":129,"tags":132},[128],"IoT",[28,130,128,131],"internet of things","edge computing",[28],{"code":134,"score":13,"supported":116,"title":135,"abstract":136,"tags":141},"F4",[],[137,138,139,140],"农药","除草","pesticide","crop protection",[102],{"code":40,"score":21,"supported":116,"title":143,"abstract":144,"tags":147},[],[145,146],"精准农业","precision agriculture",[27],{"code":149,"score":81,"supported":116,"title":150,"abstract":151,"tags":154},"A2",[],[152,153],"小麦","wheat",[],{"code":156,"score":81,"supported":116,"title":157,"abstract":158,"tags":161},"A3",[],[159,160],"玉米","maize",[],{"code":163,"score":81,"supported":116,"title":164,"abstract":165,"tags":168},"A4",[],[166,167],"大豆","soybean",[],{"code":170,"score":81,"supported":116,"title":171,"abstract":172,"tags":175},"B1",[],[173,174],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[178,179],"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","10.25258\u002Fijddt.16.76s.32",{"doi":181,"openalex_id":183,"authors":184,"venue":89,"cited_by_count":70,"oa_url":86,"card":185,"direction":76,"ingested_from":78},"W7223145687",[],{"tldr":186,"method":187,"finding":188,"direction":76,"opportunity":189},"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%。","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。","2026-10-22T11:20:18.540359Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":202,"tags":204,"primary_topic":206,"secondary_topics":210,"classification":221,"search_phrases":273,"slug":276,"view_count":70,"doi":277,"paper":278,"created_at":297},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":93,"substance":94,"depth":93,"authority":20,"freshness":200,"relevant":22,"comment":201},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。",[203],{"name":197,"url":194},[27,101,28,145,205],"气候韧性",{"code":115,"slug":207,"name":208,"path":209,"parent_code":105},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[211,215],{"code":45,"slug":212,"name":213,"path":214,"parent_code":105},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":222,"locked":14,"version":35,"method":36,"evidence":223},"classified",{"candidates":224,"agricultural_evidence":116},[225,231,235,240,246,253,260,267],{"code":115,"score":93,"supported":116,"title":226,"abstract":227,"tags":230},[123],[120,228,121,229,118,124],"机器学习","machine learning",[101],{"code":40,"score":19,"supported":116,"title":232,"abstract":233,"tags":234},[146],[145,146],[27,145],{"code":45,"score":13,"supported":116,"title":236,"abstract":237,"tags":239},[],[28,238,128,131],"边缘计算",[28],{"code":216,"score":200,"supported":116,"title":241,"abstract":242,"tags":245},[],[243,244,217],"灌溉","水资源",[],{"code":247,"score":21,"supported":116,"title":248,"abstract":249,"tags":252},"H7",[],[250,251],"气候变化","climate change",[205],{"code":254,"score":81,"supported":116,"title":255,"abstract":256,"tags":259},"H2",[],[257,258],"土壤","soil",[],{"code":261,"score":262,"supported":116,"title":263,"abstract":264,"tags":266},"A7",3,[],[265],"间作",[],{"code":268,"score":262,"supported":116,"title":269,"abstract":270,"tags":272},"F3",[],[271],"养分管理",[],[274,275],"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":277,"openalex_id":279,"authors":280,"venue":197,"cited_by_count":70,"oa_url":194,"card":291,"direction":76,"ingested_from":78},"W7223121456",[281,284,286,288],{"name":282,"orcid":283},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":285,"orcid":9},"Oluwadare OYEBODE",{"name":287,"orcid":9},"Iretiayo Adelaiye",{"name":289,"orcid":290},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":292,"method":293,"finding":294,"direction":295,"opportunity":296},"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":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":315,"secondary_topics":318,"classification":319,"search_phrases":346,"slug":349,"view_count":70,"doi":350,"paper":351,"created_at":368},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":17,"substance":18,"depth":95,"authority":20,"freshness":96,"relevant":22,"comment":308},"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。",[310],{"name":304,"url":301},[27,28,312,313,314],"土壤水分","地下物联网","柔性天线",{"code":254,"slug":258,"name":316,"path":317,"parent_code":220},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":222,"locked":14,"version":35,"method":36,"evidence":320},{"candidates":321,"agricultural_evidence":116},[322,326,330,337,341],{"code":254,"score":21,"supported":116,"title":323,"abstract":324,"tags":325},[],[257,258],[312],{"code":40,"score":21,"supported":116,"title":327,"abstract":328,"tags":329},[],[145,146],[27],{"code":331,"score":81,"supported":116,"title":332,"abstract":333,"tags":336},"E4",[],[334,335],"食品安全","food safety",[],{"code":216,"score":81,"supported":116,"title":338,"abstract":339,"tags":340},[],[243,217],[],{"code":45,"score":342,"supported":116,"title":343,"abstract":344,"tags":345},4,[],[28],[28],[347,348],"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":350,"openalex_id":352,"authors":353,"venue":304,"cited_by_count":70,"oa_url":301,"card":363,"direction":76,"ingested_from":78},"W7222978032",[354,357,360],{"name":355,"orcid":356},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":358,"orcid":359},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":361,"orcid":362},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":364,"method":365,"finding":366,"direction":76,"opportunity":367},"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":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":304,"source_url":372,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":378,"tags":380,"primary_topic":383,"secondary_topics":384,"classification":386,"search_phrases":407,"slug":410,"view_count":70,"doi":411,"paper":412,"created_at":430},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":17,"substance":93,"depth":19,"authority":20,"freshness":96,"relevant":22,"comment":377},"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。",[379],{"name":304,"url":372},[27,28,145,381,382],"节水灌溉","LoRa",{"code":45,"slug":212,"name":213,"path":214,"parent_code":105},[385],{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},{"status":222,"locked":14,"version":35,"method":36,"evidence":387},{"candidates":388,"agricultural_evidence":116},[389,393,398,402],{"code":45,"score":93,"supported":116,"title":390,"abstract":391,"tags":392},[128],[28,130,128],[28],{"code":216,"score":394,"supported":116,"title":395,"abstract":396,"tags":397},15,[217],[243,217],[381],{"code":254,"score":81,"supported":116,"title":399,"abstract":400,"tags":401},[],[257,258],[],{"code":40,"score":403,"supported":14,"title":404,"abstract":405,"tags":406},2,[],[],[27,145],[408,409],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":411,"openalex_id":413,"authors":414,"venue":304,"cited_by_count":70,"oa_url":372,"card":425,"direction":76,"ingested_from":78},"W7222959710",[415,417,419,422],{"name":416,"orcid":9},"Hanfoug Salah",{"name":418,"orcid":9},"Riad Saidi",{"name":420,"orcid":421},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":423,"orcid":424},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":426,"method":427,"finding":428,"direction":76,"opportunity":429},"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":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":9,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":439,"sources":441,"tags":443,"primary_topic":447,"secondary_topics":448,"classification":449,"search_phrases":467,"slug":470,"view_count":70,"doi":471,"paper":472,"created_at":486},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","2026-10-18T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":200,"relevant":22,"comment":440},"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。",[442],{"name":437,"url":434},[27,28,444,445,446],"水培","增强现实","数据过载",{"code":45,"slug":212,"name":213,"path":214,"parent_code":105},[],{"status":222,"locked":14,"version":35,"method":36,"evidence":450},{"candidates":451,"agricultural_evidence":116},[452,456,461],{"code":45,"score":13,"supported":116,"title":453,"abstract":454,"tags":455},[],[28,130,128],[28],{"code":40,"score":21,"supported":116,"title":457,"abstract":458,"tags":460},[],[27,459],"smart agriculture",[27],{"code":462,"score":342,"supported":116,"title":463,"abstract":464,"tags":466},"B7",[],[465],"hydroponic",[444],[468,469],"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":471,"openalex_id":473,"authors":474,"venue":437,"cited_by_count":70,"oa_url":434,"card":481,"direction":76,"ingested_from":78},"W7222865525",[475,478],{"name":476,"orcid":477},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":479,"orcid":480},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":482,"method":483,"finding":484,"direction":76,"opportunity":485},"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":488,"title":489,"url":490,"summary":491,"summary_zh":9,"content":9,"source_name":492,"source_url":490,"published_at":493,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":494,"score_detail":495,"sources":498,"tags":500,"primary_topic":9,"secondary_topics":504,"classification":505,"search_phrases":520,"slug":523,"view_count":70,"doi":524,"paper":525,"created_at":540},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":96,"substance":19,"depth":496,"authority":81,"freshness":200,"relevant":22,"comment":497},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。",[499],{"name":492,"url":490},[27,28,501,502,503],"智能灌溉","实时监测","甜菜根",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":506},{"candidates":507,"agricultural_evidence":14,"reason":49},[508,512,516],{"code":216,"score":17,"supported":116,"title":509,"abstract":510,"tags":511},[217],[217],[501],{"code":45,"score":17,"supported":116,"title":513,"abstract":514,"tags":515},[128],[128],[28],{"code":40,"score":22,"supported":14,"title":517,"abstract":518,"tags":519},[],[],[27],[521,522],"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":524,"openalex_id":526,"authors":527,"venue":492,"cited_by_count":70,"oa_url":539,"card":9,"direction":76,"ingested_from":78},"W7222511746",[528,530,532,535,537],{"name":529,"orcid":9},"Soulaiman Mwamini",{"name":531,"orcid":9},"Triphonie Uwamariya",{"name":533,"orcid":534},"Jean Nepo Nsengiyumva","https:\u002F\u002Forcid.org\u002F0000-0002-0607-1825",{"name":536,"orcid":9},"Venant Kubwimana",{"name":538,"orcid":9},"Marie Jeanne Umuhoza","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11385500\u002Flatest.pdf","2026-10-18T23:30:09.469477Z"]