[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2140":3,"related-2140":115},{"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":33,"secondary_topics":39,"classification":51,"search_phrases":89,"slug":92,"view_count":93,"doi":94,"paper":95,"created_at":114},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。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,12,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","物联网","智能灌溉","精准农业","土壤监测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[40,45],{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":52,"locked":14,"version":53,"method":54,"evidence":55},"classified","2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":58},[57,64,70,76,82],{"code":34,"score":17,"supported":58,"title":59,"abstract":60,"tags":63},true,[35],[61,62,35],"灌溉","水资源",[30],{"code":46,"score":65,"supported":58,"title":66,"abstract":68,"tags":69},15,[67],"IoT",[29,67],[29],{"code":41,"score":71,"supported":58,"title":72,"abstract":73,"tags":75},14,[42],[74,42],"土壤",[],{"code":77,"score":21,"supported":58,"title":78,"abstract":79,"tags":81},"I1",[],[31,80],"precision agriculture",[27,31],{"code":83,"score":84,"supported":58,"title":85,"abstract":86,"tags":88},"I2",4,[],[87],"artificial intelligence",[28],[90,91],"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",5,"10.22214\u002Fijraset.2026.84727",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":111,"ingested_from":113},"W7212115545",[98,100,102,104],{"name":99,"orcid":9},"Gowri M.",{"name":101,"orcid":9},"Boomika M.",{"name":103,"orcid":9},"S. Rakshana",{"name":105,"orcid":9},"Rubali R.",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-11T23:30:10.173013Z",{"total":116,"page":22,"page_size":116,"items":117},6,[118,187,288,359,414,499],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":136,"classification":139,"search_phrases":167,"slug":170,"view_count":93,"doi":171,"paper":172,"created_at":186},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":21,"substance":128,"depth":71,"authority":20,"freshness":21,"relevant":22,"comment":129},16,"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。",[131],{"name":124,"url":121},[27,29,30,133,134],"土壤墒情","节水农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[137,138],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},{"status":52,"locked":14,"version":53,"method":54,"evidence":140},{"candidates":141,"agricultural_evidence":58},[142,147,152,156,163],{"code":34,"score":143,"supported":58,"title":144,"abstract":145,"tags":146},19,[35],[61,62,35],[30,134],{"code":46,"score":17,"supported":58,"title":148,"abstract":150,"tags":151},[149],"internet of things",[29,149,67],[29],{"code":41,"score":65,"supported":58,"title":153,"abstract":154,"tags":155},[42],[74,42],[133],{"code":157,"score":158,"supported":58,"title":159,"abstract":160,"tags":162},"H8",3,[],[161],"可持续农业",[],{"code":77,"score":22,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[27],[168,169],"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":171,"openalex_id":173,"authors":174,"venue":124,"cited_by_count":106,"oa_url":121,"card":181,"direction":111,"ingested_from":113},"W7213907872",[175,177,179],{"name":176,"orcid":9},"Zakarie Abdi Mohamud",{"name":178,"orcid":9},"Rozeha Binti A. Rashid",{"name":180,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":182,"method":183,"finding":184,"direction":111,"opportunity":185},"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":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":205,"secondary_topics":209,"classification":212,"search_phrases":263,"slug":266,"view_count":106,"doi":267,"paper":268,"created_at":287},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)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":197,"depth":17,"authority":198,"freshness":199,"relevant":22,"comment":200},22,13,9,"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。",[202],{"name":193,"url":190},[27,28,29,31,204],"气候韧性",{"code":83,"slug":206,"name":207,"path":208,"parent_code":50},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[210,211],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":52,"locked":14,"version":53,"method":54,"evidence":213},{"candidates":214,"agricultural_evidence":58},[215,225,229,235,239,247,251,257],{"code":83,"score":17,"supported":58,"title":216,"abstract":217,"tags":224},[87],[218,219,220,221,222,223],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[28],{"code":77,"score":128,"supported":58,"title":226,"abstract":227,"tags":228},[80],[31,80],[27,31],{"code":46,"score":13,"supported":58,"title":230,"abstract":231,"tags":234},[],[29,232,67,233],"边缘计算","edge computing",[29],{"code":34,"score":199,"supported":58,"title":236,"abstract":237,"tags":238},[],[61,62,35],[],{"code":240,"score":241,"supported":58,"title":242,"abstract":243,"tags":246},"H7",7,[],[244,245],"气候变化","climate change",[204],{"code":41,"score":116,"supported":58,"title":248,"abstract":249,"tags":250},[],[74,42],[],{"code":252,"score":158,"supported":58,"title":253,"abstract":254,"tags":256},"A7",[],[255],"间作",[],{"code":258,"score":158,"supported":58,"title":259,"abstract":260,"tags":262},"F3",[],[261],"养分管理",[],[264,265],"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":267,"openalex_id":269,"authors":270,"venue":193,"cited_by_count":106,"oa_url":190,"card":281,"direction":111,"ingested_from":113},"W7223121456",[271,274,276,278],{"name":272,"orcid":273},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":275,"orcid":9},"Oluwadare OYEBODE",{"name":277,"orcid":9},"Iretiayo Adelaiye",{"name":279,"orcid":280},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":282,"method":283,"finding":284,"direction":285,"opportunity":286},"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":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":299,"tags":301,"primary_topic":303,"secondary_topics":309,"classification":312,"search_phrases":332,"slug":335,"view_count":336,"doi":337,"paper":338,"created_at":358},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":20,"substance":17,"depth":65,"authority":199,"freshness":21,"relevant":22,"comment":298},"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。",[300],{"name":294,"url":291},[27,29,31,32,302],"水稻种植",{"code":304,"slug":305,"name":306,"path":307,"parent_code":308},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[310,311],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},{"status":52,"locked":14,"version":53,"method":54,"evidence":313},{"candidates":314,"agricultural_evidence":58},[315,319,324,328],{"code":46,"score":17,"supported":58,"title":316,"abstract":317,"tags":318},[149],[29,149,67],[29],{"code":304,"score":65,"supported":58,"title":320,"abstract":322,"tags":323},[321],"paddy",[306,305],[302],{"code":41,"score":71,"supported":58,"title":325,"abstract":326,"tags":327},[42],[74,42],[],{"code":77,"score":21,"supported":58,"title":329,"abstract":330,"tags":331},[],[31,80],[27,31],[333,334],"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",2,"10.56581\u002Fijrerd.11.05.01-08",{"doi":337,"openalex_id":339,"authors":340,"venue":294,"cited_by_count":106,"oa_url":291,"card":353,"direction":111,"ingested_from":113},"W7220841659",[341,343,346,348,351],{"name":342,"orcid":9},"Fariha Laqania",{"name":344,"orcid":345},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":347,"orcid":9},"Ade Setiawan",{"name":349,"orcid":350},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":352,"orcid":9},"Wahyu Daradjat",{"tldr":354,"method":355,"finding":356,"direction":111,"opportunity":357},"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":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":375,"classification":377,"search_phrases":396,"slug":399,"view_count":22,"doi":400,"paper":401,"created_at":413},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","2026-10-05T00:00:00Z",49,{"impact":21,"substance":71,"depth":198,"authority":116,"freshness":21,"relevant":22,"comment":369},"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。",[371],{"name":365,"url":362},[27,29,30,31,373],"传感器",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[376],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":378},{"candidates":379,"agricultural_evidence":58},[380,384,388,392],{"code":34,"score":17,"supported":58,"title":381,"abstract":382,"tags":383},[35],[61,62,35],[30],{"code":77,"score":21,"supported":58,"title":385,"abstract":386,"tags":387},[],[31,80],[27,31],{"code":46,"score":21,"supported":58,"title":389,"abstract":390,"tags":391},[],[29,67],[373,29],{"code":41,"score":116,"supported":58,"title":393,"abstract":394,"tags":395},[],[74,42],[],[397,398],"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":400,"openalex_id":402,"authors":403,"venue":365,"cited_by_count":106,"oa_url":362,"card":408,"direction":111,"ingested_from":113},"W7220437851",[404,406],{"name":405,"orcid":9},"Mr. Bhagyesh N Chaudhari",{"name":407,"orcid":9},"Dr. Dinesh D Puri",{"tldr":409,"method":410,"finding":411,"direction":111,"opportunity":412},"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":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":422,"sources":424,"tags":426,"primary_topic":428,"secondary_topics":429,"classification":435,"search_phrases":481,"slug":484,"view_count":22,"doi":485,"paper":486,"created_at":498},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z",{"impact":17,"substance":197,"depth":143,"authority":198,"freshness":21,"relevant":22,"comment":423},"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[425],{"name":420,"url":417},[27,28,31,427,32],"无线传感器网络",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},[430,431],{"code":41,"slug":42,"name":43,"path":44,"parent_code":38},{"code":157,"slug":432,"name":433,"path":434,"parent_code":38},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":52,"locked":14,"version":53,"method":54,"evidence":436},{"candidates":437,"agricultural_evidence":58},[438,444,448,453,457,464,471,477],{"code":46,"score":65,"supported":58,"title":439,"abstract":441,"tags":443},[440],"wireless sensor",[442,440],"无线传感",[427],{"code":41,"score":71,"supported":58,"title":445,"abstract":446,"tags":447},[42],[74,42],[],{"code":157,"score":21,"supported":58,"title":449,"abstract":451,"tags":452},[450],"sustainable agriculture",[],[],{"code":77,"score":21,"supported":58,"title":454,"abstract":455,"tags":456},[],[31,80],[27,31],{"code":458,"score":116,"supported":58,"title":459,"abstract":460,"tags":463},"A3",[],[461,462],"玉米","corn",[],{"code":465,"score":116,"supported":58,"title":466,"abstract":467,"tags":470},"A4",[],[468,469],"大豆","soybean",[],{"code":472,"score":158,"supported":58,"title":473,"abstract":474,"tags":476},"F5",[],[475],"harvester",[],{"code":83,"score":22,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[28],[482,483],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":485,"openalex_id":487,"authors":488,"venue":420,"cited_by_count":106,"oa_url":492,"card":493,"direction":111,"ingested_from":113},"W7214927134",[489],{"name":490,"orcid":491},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":494,"method":495,"finding":496,"direction":111,"opportunity":497},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","2026-10-03T23:30:17.793958Z",{"id":500,"title":501,"url":502,"summary":503,"summary_zh":504,"content":9,"source_name":505,"source_url":502,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":511,"tags":513,"primary_topic":514,"secondary_topics":515,"classification":518,"search_phrases":550,"slug":553,"view_count":336,"doi":554,"paper":555,"created_at":572},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",72,{"impact":20,"substance":509,"depth":19,"authority":198,"freshness":199,"relevant":22,"comment":510},21,"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。",[512],{"name":505,"url":502},[27,28,219,29,30],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[516,517],{"code":83,"slug":206,"name":207,"path":208,"parent_code":50},{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":519},{"candidates":520,"agricultural_evidence":58},[521,525,529,533,537,542,546],{"code":34,"score":17,"supported":58,"title":522,"abstract":523,"tags":524},[35],[61,62,35],[30],{"code":83,"score":198,"supported":58,"title":526,"abstract":527,"tags":528},[87],[219],[28,219],{"code":46,"score":199,"supported":58,"title":530,"abstract":531,"tags":532},[67],[],[29],{"code":41,"score":116,"supported":58,"title":534,"abstract":535,"tags":536},[],[74,42],[],{"code":240,"score":158,"supported":58,"title":538,"abstract":539,"tags":541},[],[540],"干旱",[],{"code":157,"score":158,"supported":58,"title":543,"abstract":544,"tags":545},[],[161],[],{"code":77,"score":22,"supported":14,"title":547,"abstract":548,"tags":549},[],[],[27],[551,552],"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":554,"openalex_id":556,"authors":557,"venue":505,"cited_by_count":106,"oa_url":502,"card":567,"direction":111,"ingested_from":113},"W7213546497",[558,561,564],{"name":559,"orcid":560},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":562,"orcid":563},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":565,"orcid":566},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":568,"method":569,"finding":570,"direction":111,"opportunity":571},"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"]