[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1986":3,"related-1986":112},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":90,"slug":93,"view_count":94,"doi":95,"paper":96,"created_at":111},1986,"AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84732","The diminishing availability of water for agriculture, sub optimal irrigation methods, climate uncertainty and rising food demands have led to a demand for intelligent and sustainable solutions to water management problems. This research introduces an AI Driven Arduino and Raspberry Pi irrigation system that maximizes agricultural water use by real time sensing, intelligent decision making, and automated irrigation control. The proposed system comprises soil moisture, soil temperature, soil relative humidity, rainfall, and water level sensors to continuously monitor the field conditions. Arduino is used for interfacing with sensors and actuators, Raspberry Pi for data processing, AI based analysis, monitoring and data logging. The sensor data collected is preprocessed and analyzed with an AI\u002FML model to calculate the irrigation needs depending on soil and environmental conditions. The water pump or irrigation valve is automatically turned on or off with a relay mechanism, based on the decision generated. The experimental results show the effectiveness of the proposed method in reducing unnecessary irrigation and the utilization of the resources. With a representative prototype set of results, the AI model was able to identify 94.2% of the events correctly, whereas the automated system was able to detect around 98.6% of events, with a mean response time of 2.4 seconds. About 35% less water was used than traditional irrigation methods and still kept the soil moisture level appropriate. The proposed system uses minimal manual intervention, it facilitates real time monitoring and offers adaptive irrigation management. The research shows the potential of the integration of artificial intelligence with low cost embedded platforms to create scalable, water efficient and sustainable smart agriculture solutions.","农业可用水资源的日益减少、灌溉方法的不尽完善、气候的不确定性以及不断增长的粮食需求，催生了对水资源管理问题智能化、可持续解决方案的需求。本研究提出了一种基于人工智能驱动的Arduino与Raspberry Pi灌溉系统，通过实时感知、智能决策和自动化灌溉控制，最大化农业用水效率。该系统包含土壤湿度、土壤温度、土壤相对湿度、降雨量及水位传感器，以持续监测田间状况。其中，Arduino用于连接传感器和执行器，Raspberry Pi则负责数据处理、基于人工智能的分析、监控及数据记录。采集到的传感器数据经过预处理，并利用AI\u002FML模型进行分析，根据土壤及环境条件计算灌溉需求。基于生成的决策，通过继电器机制自动开启或关闭水泵或灌溉阀门。实验结果表明，该方法在减少不必要灌溉及资源利用方面具有显著效果。以代表性原型测试结果为例，AI模型能够正确识别94.2%的事件，而自动化系统可检测约98.6%的事件，平均响应时间为2.4秒。与传统灌溉方法相比，用水量减少了约35%，同时仍能保持适宜的土壤湿度水平。该系统所需人工干预极少，支持实时监控，并提供适应性灌溉管理。本研究展示了将人工智能与低成本嵌入式平台相结合，以构建可扩展、节水且可持续的智慧农业解决方案的潜力。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-09-08T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,20,16,2,1,"研究展示AI与低成本硬件结合的精准灌溉系统，节水35%，具实践价值，但发表于普通期刊且时效性低。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","传感器","精准灌溉","节水农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,63,70,78,85],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":56},19,true,[33],[54,55,33],"灌溉","水资源",[29,30],{"code":39,"score":58,"supported":51,"title":59,"abstract":61,"tags":62},8,[60],"sustainable agriculture",[],[],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":69},"I1",7,[],[26,68],"smart agriculture",[26],{"code":71,"score":72,"supported":51,"title":73,"abstract":74,"tags":77},"H2",6,[],[75,76],"土壤","soil",[],{"code":79,"score":80,"supported":51,"title":81,"abstract":82,"tags":84},"I2",4,[],[83],"artificial intelligence",[27],{"code":86,"score":21,"supported":14,"title":87,"abstract":88,"tags":89},"I5",[],[],[28],[91,92],"农业人工智能 智慧农业 精准灌溉 节水农业","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉节水农业-1986",3,"10.22214\u002Fijraset.2026.84732",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7211968732",[99,101],{"name":100,"orcid":9},"Priyadharshini V",{"name":102,"orcid":9},"Hemapriya",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出AI驱动的Arduino和树莓派灌溉系统，实现实时监测与自动灌溉，节水35%。","集成多种传感器，Arduino采集，树莓派运行AI\u002FML模型决策，继电器控制灌溉","系统事件识别准确率94.2%，自动检测98.6%，响应2.4秒，节水35%且维持土壤湿度。","智慧农业 \u002F 农业物联网","可探索AI模型在不同作物和气候下的泛化性，以及多节点协同和能耗优化，提升系统可扩展性。","openalex","2026-09-09T23:30:08.891805Z",{"total":72,"page":21,"page_size":72,"items":113},[114,192,303,412,488,549],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":126,"tags":128,"primary_topic":131,"secondary_topics":132,"classification":139,"search_phrases":166,"slug":169,"view_count":58,"doi":170,"paper":171,"created_at":191},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",77,{"impact":17,"substance":18,"depth":17,"authority":124,"freshness":58,"relevant":21,"comment":125},13,"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[127],{"name":120,"url":117},[26,27,129,30,130],"智能灌溉","成本效益分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[133,134],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":79,"slug":135,"name":136,"path":137,"parent_code":138},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":140},{"candidates":141,"agricultural_evidence":51},[142,146,152,158,162],{"code":32,"score":50,"supported":51,"title":143,"abstract":144,"tags":145},[33],[54,55,33],[129,30],{"code":39,"score":147,"supported":51,"title":148,"abstract":149,"tags":151},14,[60],[150,60],"可持续农业",[],{"code":79,"score":13,"supported":51,"title":153,"abstract":154,"tags":157},[],[155,156,83],"机器学习","machine learning",[27],{"code":71,"score":72,"supported":51,"title":159,"abstract":160,"tags":161},[],[75,76],[],{"code":64,"score":21,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[26],[167,168],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":170,"openalex_id":172,"authors":173,"venue":120,"cited_by_count":103,"oa_url":117,"card":186,"direction":108,"ingested_from":110},"W7214115789",[174,176,178,180,182,184],{"name":175,"orcid":9},"Venkata Suman Jami",{"name":177,"orcid":9},"Purushotham Prasad Kalisetti",{"name":179,"orcid":9},"Sampath Dakshina Murthy A.",{"name":181,"orcid":9},"Gurunadha R.",{"name":183,"orcid":9},"Hema Mamidipaka",{"name":185,"orcid":9},"Gurrapu Omprakash",{"tldr":187,"method":188,"finding":189,"direction":108,"opportunity":190},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":198,"source_url":195,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":204,"tags":206,"primary_topic":210,"secondary_topics":211,"classification":214,"search_phrases":286,"slug":289,"view_count":20,"doi":290,"paper":291,"created_at":302},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":17,"substance":18,"depth":202,"authority":124,"freshness":58,"relevant":21,"comment":203},17,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[205],{"name":198,"url":195},[26,27,207,208,209],"产量预测","可解释AI","精准农业",{"code":79,"slug":135,"name":136,"path":137,"parent_code":138},[212,213],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":215},{"candidates":216,"agricultural_evidence":51},[217,225,230,234,241,248,252,258,264,270,276,282],{"code":79,"score":218,"supported":51,"title":219,"abstract":220,"tags":224},11,[],[221,155,222,156,83,223],"目标检测","计算机视觉","computer vision",[27,208],{"code":32,"score":226,"supported":51,"title":227,"abstract":228,"tags":229},9,[],[54,55,33],[],{"code":39,"score":58,"supported":51,"title":231,"abstract":232,"tags":233},[60],[],[],{"code":235,"score":72,"supported":51,"title":236,"abstract":237,"tags":240},"A8",[],[238,239],"粮食安全","food security",[],{"code":242,"score":72,"supported":51,"title":243,"abstract":244,"tags":247},"F3",[],[245,246],"肥料","fertilizer",[],{"code":71,"score":72,"supported":51,"title":249,"abstract":250,"tags":251},[],[75,76],[],{"code":86,"score":72,"supported":51,"title":253,"abstract":254,"tags":257},[],[255,256],"物联网","internet of things",[],{"code":259,"score":80,"supported":51,"title":260,"abstract":261,"tags":263},"I3",[],[262],"遥感",[207],{"code":265,"score":94,"supported":51,"title":266,"abstract":267,"tags":269},"A7",[],[268],"间作",[],{"code":271,"score":94,"supported":51,"title":272,"abstract":273,"tags":275},"F4",[],[274],"除草",[],{"code":277,"score":94,"supported":51,"title":278,"abstract":279,"tags":281},"H7",[],[280],"气候变化",[],{"code":64,"score":20,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[26,209],[287,288],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":290,"openalex_id":292,"authors":293,"venue":198,"cited_by_count":103,"oa_url":195,"card":296,"direction":108,"ingested_from":110},"W7212616413",[294],{"name":295,"orcid":9},"Manish Maan",{"tldr":297,"method":298,"finding":299,"direction":300,"opportunity":301},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","农业人工智能与决策模型","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":310,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":311,"score_detail":312,"sources":316,"tags":318,"primary_topic":320,"secondary_topics":321,"classification":327,"search_phrases":382,"slug":385,"view_count":103,"doi":386,"paper":387,"created_at":411},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":17,"substance":313,"depth":17,"authority":314,"freshness":226,"relevant":21,"comment":315},22,12,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[317],{"name":309,"url":306},[26,27,262,319,29],"作物水分胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[322,323],{"code":79,"slug":135,"name":136,"path":137,"parent_code":138},{"code":86,"slug":324,"name":325,"path":326,"parent_code":138},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":44,"locked":14,"version":45,"method":46,"evidence":328},{"candidates":329,"agricultural_evidence":51},[330,335,339,344,350,355,362,369,376],{"code":32,"score":331,"supported":51,"title":332,"abstract":333,"tags":334},15,[33],[54,33],[29],{"code":79,"score":13,"supported":51,"title":336,"abstract":337,"tags":338},[],[155,156,83],[27],{"code":86,"score":226,"supported":51,"title":340,"abstract":341,"tags":343},[],[255,256,342],"IoT",[],{"code":64,"score":65,"supported":51,"title":345,"abstract":346,"tags":349},[],[347,348],"数字孪生","digital twin",[26],{"code":259,"score":65,"supported":51,"title":351,"abstract":352,"tags":354},[],[262,353],"remote sensing",[262],{"code":356,"score":72,"supported":51,"title":357,"abstract":358,"tags":361},"A2",[],[359,360],"小麦","wheat",[],{"code":363,"score":72,"supported":51,"title":364,"abstract":365,"tags":368},"A3",[],[366,367],"玉米","maize",[],{"code":370,"score":72,"supported":51,"title":371,"abstract":372,"tags":375},"A5",[],[373,374],"马铃薯","potato",[],{"code":377,"score":94,"supported":51,"title":378,"abstract":379,"tags":381},"B2",[],[380],"葡萄",[],[383,384],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":386,"openalex_id":388,"authors":389,"venue":309,"cited_by_count":103,"oa_url":306,"card":405,"direction":108,"ingested_from":110},"W7223088921",[390,393,396,399,402],{"name":391,"orcid":392},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":394,"orcid":395},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":397,"orcid":398},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":400,"orcid":401},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":403,"orcid":404},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":406,"method":407,"finding":408,"direction":409,"opportunity":410},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":420,"is_selected":14,"score":421,"score_detail":422,"sources":425,"tags":429,"primary_topic":432,"secondary_topics":433,"classification":435,"search_phrases":465,"slug":468,"view_count":103,"doi":469,"paper":470,"created_at":487},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":313,"substance":423,"depth":202,"authority":124,"freshness":103,"relevant":21,"comment":424},21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[426,427],{"name":418,"url":415},{"name":418,"url":428},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,27,430,431,29],"农业物联网","区块链溯源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[434],{"code":86,"slug":324,"name":325,"path":326,"parent_code":138},{"status":44,"locked":14,"version":45,"method":46,"evidence":436},{"candidates":437,"agricultural_evidence":51},[438,442,446,450,456,461],{"code":32,"score":331,"supported":51,"title":439,"abstract":440,"tags":441},[33],[54,33],[29],{"code":86,"score":13,"supported":51,"title":443,"abstract":444,"tags":445},[],[255,256,342],[430],{"code":79,"score":80,"supported":51,"title":447,"abstract":448,"tags":449},[],[83],[27],{"code":451,"score":94,"supported":51,"title":452,"abstract":453,"tags":455},"G2",[],[454],"小农户",[],{"code":457,"score":21,"supported":14,"title":458,"abstract":459,"tags":460},"E4",[],[],[431],{"code":64,"score":21,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[26],[466,467],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":469,"openalex_id":471,"authors":472,"venue":418,"cited_by_count":103,"oa_url":415,"card":481,"direction":108,"ingested_from":110},"W7223034914",[473,476,478],{"name":474,"orcid":475},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":477,"orcid":9},"Dr.T.Baskar",{"name":479,"orcid":480},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":482,"method":483,"finding":484,"direction":485,"opportunity":486},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":489,"title":490,"url":491,"summary":492,"summary_zh":493,"content":9,"source_name":494,"source_url":491,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":503,"secondary_topics":504,"classification":506,"search_phrases":525,"slug":528,"view_count":103,"doi":529,"paper":530,"created_at":548},4888,"A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74935-0","Smart irrigation offers a great avenue for better water use efficiency, and farming sustainability. However, most of the studies conducted so far tend to concentrate on the monitoring systems and platforms of the Internet of Things (IoT) or on the prediction models based on Artificial Intelligence (AI) (but not on both). To address this gap, this research study proposes an Artificial Intelligence (AI)-Internet of Things (IoT) system to predict the water consumption requirements and control the energy produced by a PV system along with a battery State of Charge (SOC) management system, Maximum Power Point Tracking (MPPT) algorithm, adaptive pump-speed control, and conceptual IoT communication architecture. A dataset of real environmental data from the NASA POWER database was used to test the framework for Egypt’s New Delta region. The LSTM model was able to classify the irrigation pump operating states with an accuracy of 96.11%, a recall of 0.9820, a precision of 0.9100, and an F1-score of 0.9446. It had a better performance than the two models, Artificial Neural Network (ANN) and Support Vector Machine (SVM) when compared and robustness testing with various independent training runs showed a stable performance in predicting. An estimated annual water-saving rate of 28.57% was obtained relative to the assumed conventional fixed-schedule baseline, while motor energy use was reduced by 74.74% through adaptive pump-speed control. A sensitivity analysis was also performed, showing that the model was stable to moderate variations in environmental and operating conditions. Combined, these findings suggest that the use of AI-based modelling for renewable energy management in the context of an irrigation simulation model built in MATLAB could be a valuable asset in improving irrigation efficiency and lowering water and energy use, which is a promising start for future implementation of hardware versions of intelligent, solar-powered irrigation devices.","智能灌溉为提高用水效率和农业可持续性提供了重要途径。然而，迄今为止大多数研究倾向于集中在物联网（IoT）的监测系统和平台，或基于人工智能（AI）的预测模型（但很少两者兼顾）。为弥补这一空白，本研究提出了一种人工智能（AI）-物联网（IoT）系统，用于预测用水需求并控制光伏系统产生的能量，同时包含电池荷电状态（SOC）管理系统、最大功率点跟踪（MPPT）算法、自适应泵速控制以及概念性物联网通信架构。研究采用NASA POWER数据库中的真实环境数据集，在埃及新三角洲地区对该框架进行了测试。LSTM模型能够对灌溉泵运行状态进行分类，准确率达到96.11%，召回率为0.9820，精确率为0.9100，F1分数为0.9446。与人工神经网络（ANN）和支持向量机（SVM）两个模型相比，该模型表现更优，且通过多次独立训练运行进行的鲁棒性测试表明其在预测方面具有稳定性能。相对于假设的传统固定调度基线，估计年节水率为28.57%，而通过自适应泵速控制，电机能耗降低了74.74%。研究还进行了敏感性分析，表明该模型对环境和运行条件的中等程度变化具有稳定性。综合来看，这些发现表明，在MATLAB中构建的灌溉仿真模型背景下，将基于AI的建模用于可再生能源管理，可能是提高灌溉效率、降低水和能源消耗的有价值手段，这为未来实现智能太阳能灌溉设备的硬件版本提供了一个有前景的开端。","Scientific Reports","2026-10-12T00:00:00Z",78,{"impact":19,"substance":313,"depth":17,"authority":147,"freshness":58,"relevant":21,"comment":498},"该研究提出AI-IoT融合的太阳能灌溉仿真框架，LSTM预测精度达96.11%，节水28.57%、节能74.74%，方法新颖且数据可靠，对智慧农业与农业能源管理有参考价值。",[500],{"name":494,"url":491},[26,27,129,30,502],"光伏农业",{"code":86,"slug":324,"name":325,"path":326,"parent_code":138},[505],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":507},{"candidates":508,"agricultural_evidence":51},[509,513,517,521],{"code":86,"score":202,"supported":51,"title":510,"abstract":511,"tags":512},[342],[255,256,342],[],{"code":32,"score":19,"supported":51,"title":514,"abstract":515,"tags":516},[33],[54,33],[129,30],{"code":79,"score":80,"supported":51,"title":518,"abstract":519,"tags":520},[],[83],[27],{"code":64,"score":21,"supported":14,"title":522,"abstract":523,"tags":524},[],[],[26],[526,527],"MATLAB AI-IoT 太阳能灌溉","LSTM 智能灌溉 埃及新三角洲","MATLABAI-IoT太阳能灌溉-4888","10.1038\u002Fs41598-026-74935-0",{"doi":529,"openalex_id":531,"authors":532,"venue":494,"cited_by_count":103,"oa_url":542,"card":543,"direction":108,"ingested_from":110},"W7221571268",[533,536,539],{"name":534,"orcid":535},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":537,"orcid":538},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":540,"orcid":541},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":544,"method":545,"finding":546,"direction":108,"opportunity":547},"提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。","NASA POWER环境数据，LSTM对比ANN\u002FSVM，含MPPT与自适应泵速","LSTM分类准确率96.11%，节水28.57%，电机能耗降74.74%。","可延伸至真实硬件部署与多区域验证，并融合实时气象与土壤墒情数据。","2026-10-14T23:30:13.060384Z",{"id":550,"title":551,"url":552,"summary":553,"summary_zh":554,"content":9,"source_name":555,"source_url":552,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":556,"sources":558,"tags":560,"primary_topic":562,"secondary_topics":563,"classification":564,"search_phrases":594,"slug":597,"view_count":20,"doi":598,"paper":599,"created_at":616},4810,"Smart water management in agriculture: convergence of sensors, imaging, AI, precision irrigation, and robotics systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrwa.2026.1973897","Agricultural irrigation systems can bear the heavy losses of water through runoff, evaporation, and deep percolation. Crop water requirements vary with crop species, growth stage, soil properties, evapotranspiration, weather, and plant water status; therefore, irrigation scheduling based on a single parameter is insufficient. Conventional water management systems often rely on uniform application, fixed scheduling, and limited real-time information, which overlooks spatial and temporal variation in soil, crop, and weather conditions and can result in water loss, over-irrigation, and under-irrigation. This review highlights studies on the integration of AI-driven sensing and imaging technologies, IoT-based precision irrigation, variable-rate technologies, digital twins, and agricultural robotics for crop water management. These approaches integrate field, crop, weather, and spatial information to support water-demand assessment, crop-stress monitoring, irrigation scheduling, site-specific application, and automated water management. The reviewed studies report water savings, improved water-use efficiency, crop performance, and more site-specific irrigation and input application across different crops and irrigation systems. The review also highlights the limitations of these systems, including sensing reliability, model transferability, connectivity, interoperability, cybersecurity, investment costs, and environmental effects. Overall, the integration of these technologies provides potential for timely and field-specific irrigation decisions and supports efficient and climate-resilient water management in agriculture.","农业灌溉系统会因径流、蒸发和深层渗漏而承受严重的水量损失。作物需水量随作物种类、生长阶段、土壤性质、蒸散量、天气和植株水分状况而变化；因此，仅基于单一参数的灌溉调度是不够的。传统的水管理系统往往依赖均匀灌溉、固定调度和有限的实时信息，忽视了土壤、作物和天气条件的时空变异，可能导致水分损失、过度灌溉和灌溉不足。本文综述了将人工智能驱动的传感与成像技术、基于物联网的精准灌溉、变量灌溉技术、数字孪生和农业机器人集成应用于作物水分管理的研究。这些方法整合了田块、作物、天气和空间信息，以支持需水评估、作物胁迫监测、灌溉调度、定点变量灌溉和自动化水分管理。所综述的研究报告了在不同作物和灌溉系统中实现的节水、水分利用效率提升、作物表现改善以及更具田块针对性的灌溉和投入品施用。本文还指出了这些系统的局限性，包括传感可靠性、模型可迁移性、连通性、互操作性、网络安全、投资成本和环境影响。总体而言，这些技术的集成可为及时且针对具体田块的灌溉决策提供可能，并支持农业中高效且具有气候韧性的水分管理。","Frontiers in Water",{"impact":17,"substance":18,"depth":17,"authority":124,"freshness":226,"relevant":21,"comment":557},"系统综述AI传感、物联网精准灌溉与农业机器人集成，方法视角新、结论可靠，对智慧农业水资源管理有参考价值。",[559],{"name":555,"url":552},[26,27,561,347,29],"农业机器人",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":565},{"candidates":566,"agricultural_evidence":51},[567,572,576,580,585,590],{"code":32,"score":17,"supported":51,"title":568,"abstract":569,"tags":571},[33],[54,33,570],"evapotranspiration",[29],{"code":71,"score":72,"supported":51,"title":573,"abstract":574,"tags":575},[],[75,76],[],{"code":86,"score":72,"supported":51,"title":577,"abstract":578,"tags":579},[],[255,342],[],{"code":64,"score":581,"supported":51,"title":582,"abstract":583,"tags":584},5,[],[347],[347,26],{"code":586,"score":80,"supported":51,"title":587,"abstract":588,"tags":589},"F6",[],[561],[561],{"code":79,"score":21,"supported":14,"title":591,"abstract":592,"tags":593},[],[],[27],[595,596],"精准灌溉 传感器 人工智能","农业机器人 水资源管理","精准灌溉传感器人工智能-4810","10.3389\u002Ffrwa.2026.1973897",{"doi":598,"openalex_id":600,"authors":601,"venue":555,"cited_by_count":103,"oa_url":610,"card":611,"direction":108,"ingested_from":110},"W7221563526",[602,605,607],{"name":603,"orcid":604},"Sachin Ghanghas","https:\u002F\u002Forcid.org\u002F0000-0001-9231-2527",{"name":606,"orcid":9},"Naveen Sihag",{"name":608,"orcid":609},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fwater\u002Farticles\u002F10.3389\u002Ffrwa.2026.1973897\u002Fpdf",{"tldr":612,"method":613,"finding":614,"direction":108,"opportunity":615},"综述AI传感、成像、物联网精准灌溉、数字孪生与机器人集成用于作物水分管理。","综述融合AI传感成像、IoT精准灌溉、变量技术、数字孪生与农业机器人。","集成方案可节水、提升水分利用效率与作物表现，但存在传感可靠性、模型迁移等局限。","可研究多源传感与数字孪生融合的跨区域模型迁移及低成本、安全互操作的灌溉决策系统。","2026-10-13T23:30:10.375774Z"]