[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3130":3,"related-3130":68},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":67},3130,"Closed-loop autonomous scheduling of multi-level irrigation canal-gate systems using an LLM-agent framework: prompting strategies and model heterogeneity","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112444","Closed-loop autonomous scheduling of multi-level irrigation canal-gate systems using an LLM-agent framework: prompting strategies and model heterogeneity。Computers and Electronics in Agriculture","基于LLM-agent框架的多级灌溉渠闸系统闭环自主调度：提示策略与模型异质性。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-22T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,20,14,1,"核心期刊论文，将大模型智能体框架用于多级灌溉渠系闸门闭环自主调度，方法新颖、时效性强，对智慧灌溉有参考价值。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","智能灌溉","大模型","渠系闸门",[31,32],"LLM-agent 灌溉渠系 闸门调度","Computers and Electronics in Agriculture 智能灌溉","LLM-agent灌溉渠系闸门调度-3130",0,"10.1016\u002Fj.compag.2026.112444",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":9,"card":60,"direction":64,"ingested_from":66},"W7213982866",[39,41,43,46,48,51,53,55,57],{"name":40,"orcid":9},"Chen Junyu",{"name":42,"orcid":9},"Wang Haiyu",{"name":44,"orcid":45},"Junzeng Xu","https:\u002F\u002Forcid.org\u002F0000-0003-1467-7883",{"name":47,"orcid":9},"Zhang Binyang",{"name":49,"orcid":50},"Haoyang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-9253-5824",{"name":52,"orcid":9},"Ye ZiJian",{"name":54,"orcid":9},"Zhang Zhonglili",{"name":56,"orcid":9},"Liu Xiaoyin",{"name":58,"orcid":59},"Rong Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8680-184X",{"tldr":61,"method":62,"finding":63,"direction":64,"opportunity":65},"提出LLM智能体框架实现多级灌溉渠闸系统闭环自主调度，并比较提示策略与模型异质性。","基于LLM智能体框架，采用不同提示策略与多种大模型进行渠闸调度实验。","提示策略和模型选择显著影响多级渠闸闭环调度的自主决策效果。","农业人工智能与决策模型","可探索LLM智能体与水文模型耦合、多闸协同实时调度及鲁棒性验证。","openalex","2026-09-22T23:30:01.931736Z",{"total":69,"page":20,"page_size":69,"items":70},6,[71,116,140,169,191,229],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":9,"source_name":77,"source_url":74,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":87,"tags":89,"search_phrases":92,"slug":95,"view_count":34,"doi":96,"paper":97,"created_at":115},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":81,"substance":82,"depth":83,"authority":84,"freshness":85,"relevant":20,"comment":86},12,21,17,13,9,"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。",[88],{"name":77,"url":74},[25,26,90,91,27],"机器学习","物联网",[93,94],"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":96,"openalex_id":98,"authors":99,"venue":77,"cited_by_count":34,"oa_url":74,"card":109,"direction":113,"ingested_from":66},"W7213546497",[100,103,106],{"name":101,"orcid":102},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":104,"orcid":105},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":107,"orcid":108},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"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。","智慧农业 \u002F 农业物联网","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":117,"title":118,"url":119,"summary":120,"summary_zh":9,"content":9,"source_name":121,"source_url":9,"published_at":122,"category":123,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":129,"tags":131,"search_phrases":135,"slug":138,"view_count":34,"doi":9,"paper":9,"created_at":139},2815,"WAFI2026人工智能与农业论坛:中国方案助力农民种得好、种得起、种得稳、种得赚","https:\u002F\u002Fbaike.baidu.com\u002Fitem\u002F2026%E4%B8%96%E7%95%8C%E5%86%9C%E4%B8%9A%E7%A7%91%E6%8A%80%E5%88%9B%E6%96%B0%E5%A4%A7%E4%BC%9A\u002F68651190","9月16日WAFI2026举行\"人工智能与农业论坛\",中国农业大学全球食物经济与政策研究院院长樊胜根提出\"人工智能如何造福农民\"议题。论坛介绍神农大模型(2023年1.0版到2025年3.0版,3.0版为\"小麦育种智能助手\",可识别70类、600余种病虫害,已在非洲落地)、农业食物经济与政策AI模型、北大荒\"未来农场\"平台(覆盖111个农场、接入8.4万台智能装备、为60万种植户服务)、北京市\"智京园\"智慧设施管控技术体系等案例。","百度百科 \u002F 中国农业大学","2026-09-16T00:00:00Z","报道",86,{"impact":126,"substance":82,"depth":83,"authority":19,"freshness":127,"relevant":20,"comment":128},26,8,"国际论坛上集中展示神农大模型、北大荒未来农场等中国AI农业方案，案例数据具体、信源权威，时效性强，值得进入每日精选。",[130],{"name":121,"url":119},[25,26,132,133,28,134],"未来农场","智能育种","病虫害识别",[136,137],"农业人工智能 病虫害识别 智慧农业 智能育种","农业人工智能 病虫害识别","农业人工智能病虫害识别智慧农业智能育种-2815","2026-09-18T00:03:24.639818Z",{"id":141,"title":142,"url":143,"summary":144,"summary_zh":9,"content":9,"source_name":145,"source_url":9,"published_at":146,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":147,"score_detail":148,"sources":151,"tags":153,"search_phrases":156,"slug":159,"view_count":34,"doi":9,"paper":160,"created_at":168},2610,"整合人工智能、物联网与遥感技术的大田作物智能灌溉管理 综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260913082658847.htm","发表于Biosystems Engineering。对人工智能(AI)、物联网(IoT)和遥感(RS)技术在灌溉管理中的应用进行全面且结构化分析，特别是在优化基于天气、土壤和作物的灌溉调度方面。智能灌溉系统实现了水资源节约(用水量减少高达20-60%)、降低能源消耗和提高作物生产力。未来研究应优先考虑成本效益高的传感器开发和用户友好的AI界面。","Biosystems Engineering","2026-09-13T01:00:00Z",83,{"impact":149,"substance":82,"depth":17,"authority":19,"freshness":127,"relevant":20,"comment":150},22,"核心期刊综述，系统梳理AI、物联网与遥感在大田灌溉调度中的融合应用，给出节水20-60%等量化结论，对智慧农业技术路线有参考价值。",[152],{"name":145,"url":143},[25,26,154,27,155],"农业物联网","遥感监测",[157,158],"农业人工智能 农业物联网 智慧农业 智能灌溉","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业智能灌溉-2610",{"doi":9,"openalex_id":9,"authors":161,"venue":9,"cited_by_count":34,"oa_url":9,"card":162,"direction":113,"ingested_from":167},[],{"tldr":163,"method":164,"finding":165,"direction":113,"opportunity":166},"综述AI、物联网与遥感在大田作物智能灌溉调度中的应用与成效。","结构化综述AI、IoT、RS在基于天气、土壤和作物的灌溉调度中的应用。","智能灌溉可节水20-60%，降低能耗并提高作物生产力。","低成本传感器与用户友好AI界面是落地瓶颈，可研究轻量化模型与低成本感知方案。","agent","2026-09-16T00:03:52.381950Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":9,"content":9,"source_name":174,"source_url":9,"published_at":175,"category":123,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":183,"search_phrases":186,"slug":189,"view_count":34,"doi":9,"paper":9,"created_at":190},2351,"'AI+'助力农林产业智变:2026服贸会集中展示智慧农业落地应用","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F13\u002F991028309.html","2026年中国国际服务贸易交易会上，中粮集团展示玉米质量智能检验应用入选国资委首批央企人工智能战略性高价值场景；广西展示已在马来西亚榴莲主产区规模化落地的榴莲大模型；中国铁塔在北京西山试验林场构建空地一体林草防火监测体系，林上火情监控覆盖率提升至40%以上、重点林下区域达95%。","中国农网 | 2026-09-13","2026-09-13T00:00:00Z",82,{"impact":178,"substance":18,"depth":179,"authority":84,"freshness":85,"relevant":20,"comment":180},24,16,"服贸会集中展示AI在玉米质检、榴莲大模型、林草防火等场景的落地应用，案例具体、数据明确，具备产业级参考价值。",[182],{"name":174,"url":172},[25,26,184,28,185],"农业遥感","林草防火",[187,188],"农业人工智能 农业遥感 智慧农业 林草防火","农业人工智能 农业遥感","农业人工智能农业遥感智慧农业林草防火-2351","2026-09-14T00:06:25.997866Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":202,"tags":204,"search_phrases":207,"slug":210,"view_count":34,"doi":211,"paper":212,"created_at":228},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。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",75,{"impact":17,"substance":18,"depth":83,"authority":81,"freshness":127,"relevant":20,"comment":201},"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。",[203],{"name":197,"url":194},[25,26,91,27,205,206],"精准农业","土壤监测",[208,209],"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":211,"openalex_id":213,"authors":214,"venue":197,"cited_by_count":34,"oa_url":194,"card":223,"direction":113,"ingested_from":66},"W7212115545",[215,217,219,221],{"name":216,"orcid":9},"Gowri M.",{"name":218,"orcid":9},"Boomika M.",{"name":220,"orcid":9},"S. Rakshana",{"name":222,"orcid":9},"Rubali R.",{"tldr":224,"method":225,"finding":226,"direction":113,"opportunity":227},"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":230,"title":231,"url":232,"summary":233,"summary_zh":9,"content":9,"source_name":145,"source_url":232,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":240,"search_phrases":241,"slug":242,"view_count":34,"doi":243,"paper":244,"created_at":264},2024,"Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104589","Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review。Biosystems Engineering","2026-09-09T00:00:00Z",78,{"impact":17,"substance":18,"depth":83,"authority":19,"freshness":85,"relevant":20,"comment":237},"核心期刊综述，系统梳理AI、物联网与遥感融合的田间作物智能灌溉研究进展，对智慧农业技术路线有参考价值，但属综述类成果、非突破性原创，适合进入每日精选。",[239],{"name":145,"url":232},[25,26,154,27,155],[157,158],"农业人工智能农业物联网智慧农业智能灌溉-2024","10.1016\u002Fj.biosystemseng.2026.104589",{"doi":243,"openalex_id":245,"authors":246,"venue":145,"cited_by_count":34,"oa_url":9,"card":9,"direction":9,"ingested_from":66},"W7212048658",[247,250,252,254,256,258,261],{"name":248,"orcid":249},"Ehab H. Hegazi","https:\u002F\u002Forcid.org\u002F0000-0002-1468-1420",{"name":251,"orcid":9},"Jian Liu",{"name":253,"orcid":9},"Ruixia Ai",{"name":255,"orcid":9},"Lin Liu",{"name":257,"orcid":9},"Xuemei Liu",{"name":259,"orcid":260},"Jin Yuan","https:\u002F\u002Forcid.org\u002F0000-0002-5803-6626",{"name":262,"orcid":263},"G. Papadakis","https:\u002F\u002Forcid.org\u002F0000-0002-1805-5056","2026-09-10T23:30:05.849251Z"]