[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5552":3,"related-5552":110},{"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":32,"secondary_topics":38,"classification":50,"search_phrases":91,"slug":94,"view_count":95,"doi":96,"paper":97,"created_at":109},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,16,12,9,1,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","农业物联网","遥感","智慧灌溉","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,71,77,83],{"code":33,"score":57,"supported":58,"title":59,"abstract":60,"tags":63},19,true,[34],[61,62,34],"灌溉","水资源",[30,31],{"code":46,"score":17,"supported":58,"title":65,"abstract":67,"tags":70},[66],"IoT",[68,69,66],"物联网","internet of things",[28],{"code":40,"score":72,"supported":58,"title":73,"abstract":75,"tags":76},15,[74],"remote sensing",[29,74],[29],{"code":78,"score":20,"supported":58,"title":79,"abstract":81,"tags":82},"I2",[80],"artificial intelligence",[80],[27],{"code":84,"score":85,"supported":58,"title":86,"abstract":87,"tags":90},"H8",6,[],[88,89],"可持续农业","sustainable agriculture",[],[92,93],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552",0,"10.5281\u002Fzenodo.23531020",{"doi":96,"openalex_id":98,"authors":99,"venue":10,"cited_by_count":95,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7223090389",[100],{"name":101,"orcid":9},"Patil",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","openalex","2026-10-22T11:20:18.028252Z",{"total":85,"page":22,"page_size":85,"items":111},[112,223,300,364,434,500],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":136,"search_phrases":193,"slug":196,"view_count":95,"doi":197,"paper":198,"created_at":222},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",79,{"impact":17,"substance":121,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":122},22,"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。",[124],{"name":118,"url":115},[126,27,29,127,128],"智慧农业","作物水分胁迫","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[131,135],{"code":78,"slug":132,"name":133,"path":134,"parent_code":44},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":137},{"candidates":138,"agricultural_evidence":58},[139,143,149,153,161,165,172,179,186],{"code":33,"score":72,"supported":58,"title":140,"abstract":141,"tags":142},[34],[61,34],[128],{"code":78,"score":13,"supported":58,"title":144,"abstract":145,"tags":148},[],[146,147,80],"机器学习","machine learning",[27],{"code":46,"score":21,"supported":58,"title":150,"abstract":151,"tags":152},[],[68,69,66],[],{"code":154,"score":155,"supported":58,"title":156,"abstract":157,"tags":160},"I1",7,[],[158,159],"数字孪生","digital twin",[126],{"code":40,"score":155,"supported":58,"title":162,"abstract":163,"tags":164},[],[29,74],[29],{"code":166,"score":85,"supported":58,"title":167,"abstract":168,"tags":171},"A2",[],[169,170],"小麦","wheat",[],{"code":173,"score":85,"supported":58,"title":174,"abstract":175,"tags":178},"A3",[],[176,177],"玉米","maize",[],{"code":180,"score":85,"supported":58,"title":181,"abstract":182,"tags":185},"A5",[],[183,184],"马铃薯","potato",[],{"code":187,"score":188,"supported":58,"title":189,"abstract":190,"tags":192},"B2",3,[],[191],"葡萄",[],[194,195],"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":197,"openalex_id":199,"authors":200,"venue":118,"cited_by_count":95,"oa_url":115,"card":216,"direction":106,"ingested_from":108},"W7223088921",[201,204,207,210,213],{"name":202,"orcid":203},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":205,"orcid":206},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":208,"orcid":209},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":211,"orcid":212},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":214,"orcid":215},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":217,"method":218,"finding":219,"direction":220,"opportunity":221},"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":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":9,"source_name":10,"source_url":226,"published_at":229,"category":12,"cover_url":9,"hotness":230,"is_selected":14,"score":231,"score_detail":232,"sources":237,"tags":241,"primary_topic":243,"secondary_topics":244,"classification":246,"search_phrases":277,"slug":280,"view_count":95,"doi":281,"paper":282,"created_at":299},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。","2026-11-25T00:00:00Z",25,73,{"impact":121,"substance":233,"depth":234,"authority":235,"freshness":95,"relevant":22,"comment":236},21,17,13,"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。",[238,239],{"name":10,"url":226},{"name":10,"url":240},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[126,27,28,242,128],"区块链溯源",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[245],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":247},{"candidates":248,"agricultural_evidence":58},[249,253,257,262,268,273],{"code":33,"score":72,"supported":58,"title":250,"abstract":251,"tags":252},[34],[61,34],[128],{"code":46,"score":13,"supported":58,"title":254,"abstract":255,"tags":256},[],[68,69,66],[28],{"code":78,"score":258,"supported":58,"title":259,"abstract":260,"tags":261},4,[],[80],[27],{"code":263,"score":188,"supported":58,"title":264,"abstract":265,"tags":267},"G2",[],[266],"小农户",[],{"code":269,"score":22,"supported":14,"title":270,"abstract":271,"tags":272},"E4",[],[],[242],{"code":154,"score":22,"supported":14,"title":274,"abstract":275,"tags":276},[],[],[126],[278,279],"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":281,"openalex_id":283,"authors":284,"venue":10,"cited_by_count":95,"oa_url":226,"card":293,"direction":106,"ingested_from":108},"W7223034914",[285,288,290],{"name":286,"orcid":287},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":289,"orcid":9},"Dr.T.Baskar",{"name":291,"orcid":292},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":294,"method":295,"finding":296,"direction":297,"opportunity":298},"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":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":340,"slug":343,"view_count":95,"doi":344,"paper":345,"created_at":363},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":121,"depth":17,"authority":310,"freshness":311,"relevant":22,"comment":312},14,8,"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。",[314],{"name":306,"url":303},[126,27,316,31,317],"智能灌溉","光伏农业",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[320],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":322},{"candidates":323,"agricultural_evidence":58},[324,328,332,336],{"code":46,"score":234,"supported":58,"title":325,"abstract":326,"tags":327},[66],[68,69,66],[],{"code":33,"score":19,"supported":58,"title":329,"abstract":330,"tags":331},[34],[61,34],[316,31],{"code":78,"score":258,"supported":58,"title":333,"abstract":334,"tags":335},[],[80],[27],{"code":154,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},[],[],[126],[341,342],"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":344,"openalex_id":346,"authors":347,"venue":306,"cited_by_count":95,"oa_url":357,"card":358,"direction":106,"ingested_from":108},"W7221571268",[348,351,354],{"name":349,"orcid":350},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":352,"orcid":353},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":355,"orcid":356},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":359,"method":360,"finding":361,"direction":106,"opportunity":362},"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":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":230,"is_selected":14,"score":372,"score_detail":373,"sources":375,"tags":379,"primary_topic":381,"secondary_topics":382,"classification":384,"search_phrases":412,"slug":415,"view_count":22,"doi":9,"paper":416,"created_at":433},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",53,{"impact":20,"substance":310,"depth":235,"authority":85,"freshness":311,"relevant":22,"comment":374},"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[376,377],{"name":370,"url":367},{"name":370,"url":378},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[126,27,28,380,128],"边缘计算",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[383],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":385},{"candidates":386,"agricultural_evidence":58},[387,391,397,401,405],{"code":46,"score":57,"supported":58,"title":388,"abstract":389,"tags":390},[66],[68,69,66],[28,380],{"code":154,"score":72,"supported":58,"title":392,"abstract":394,"tags":396},[393],"precision agriculture",[395,393],"精准农业",[126],{"code":33,"score":13,"supported":58,"title":398,"abstract":399,"tags":400},[],[61,62,34],[128],{"code":78,"score":155,"supported":58,"title":402,"abstract":403,"tags":404},[],[146,147],[27],{"code":406,"score":85,"supported":58,"title":407,"abstract":408,"tags":411},"H2",[],[409,410],"土壤","soil",[],[413,414],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":9,"openalex_id":417,"authors":418,"venue":370,"cited_by_count":95,"oa_url":367,"card":428,"direction":106,"ingested_from":108},"W7221411136",[419,421,423,425],{"name":420,"orcid":9},"Om Gaikwad",{"name":422,"orcid":9},"Shriraj Chavan",{"name":424,"orcid":9},"Krushnadev Gaikwad",{"name":426,"orcid":427},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":429,"method":430,"finding":431,"direction":106,"opportunity":432},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":442,"sources":444,"tags":446,"primary_topic":448,"secondary_topics":449,"classification":452,"search_phrases":481,"slug":484,"view_count":258,"doi":9,"paper":485,"created_at":499},4076,"Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning","https:\u002F\u002Fwajes.isroset.org\u002Findex.php\u002Fj\u002Farticle\u002Fview\u002F208","Irrigation decisions on smallholder farms are still taken subjectively, and both root-zone dehydration and over-saturation measurably depress yield, making low-cost automated plant-stress diagnosis an urgent requirement in water-scarce agricultural regions. This study designs, implements and validates a decentralized system that closes the control loop between field sensing and irrigation actuation by combining Internet of Things (IoT) hardware with a cloud-hosted predictive Machine Learning (ML) model. The edge node is built on an ESP32 system-on-chip integrated with a calibrated capacitive soil-moisture probe and a DHT22 temperature and humidity array. Serialized telemetry is transmitted over a non-blocking, encrypted wireless channel to a Flask web service, where a 150-tree Random Forest classifier trained on 5,000 labelled multi-sensor records assigns the plant to one of three physiological states Optimal, Temperature Stress or Dehydration Stress and returns an actuator control bit that drives a micro-irrigation pump. An engineered Evapotranspiration Proxy Index couples ambient temperature and relative humidity into a single transpiration-sensitive feature. Capacitive calibration was validated against the gravimetric standard on ten soil cores, yielding a Pearson correlation coefficient of 0.948. Over a continuous forty-eight-hour open-air evaluation the classifier achieved an overall accuracy of 96.8%, with 98.2% precision and 97.9% recall on the critical Dehydration Stress class, and a mean end-to-end round-trip latency of 1.84 seconds. Deep-sleep duty cycling reduced the mean node current to 0.65 mA, corresponding to an estimated 159 days of unattended operation on a single 2,500 mAh cell. The results demonstrate that a low-cost edge–cloud architecture can deliver dependable, timely and energy-sustainable irrigation control for micro-precision agriculture without server-grade hardware.","小农户的灌溉决策仍依赖主观判断，而根区脱水和过度饱和均会显著降低产量，这使得低成本自动化植物胁迫诊断成为缺水农业地区的迫切需求。本研究设计、实现并验证了一种去中心化系统，通过将物联网（IoT）硬件与云端预测机器学习（ML）模型相结合，闭合了田间传感与灌溉执行之间的控制回路。边缘节点基于ESP32片上系统构建，集成了经过校准的电容式土壤水分探头和DHT22温湿度传感器阵列。序列化遥测数据通过非阻塞加密无线信道传输至Flask Web服务，在此处，一个基于5,000条带标签多传感器记录训练的150棵树的随机森林分类器将植物判定为三种生理状态之一——最优、温度胁迫或脱水胁迫——并返回驱动微灌泵的执行器控制位。所设计的蒸散代理指数将环境温度和相对湿度耦合为单一蒸腾敏感特征。电容校准在十个土芯上与重量法标准进行验证，得到皮尔逊相关系数为0.948。在连续四十八小时的户外评估中，分类器总体准确率达到96.8%，在关键的脱水胁迫类别上精确率为98.2%、召回率为97.9%，平均端到端往返延迟为1.84秒。深度睡眠占空比控制将节点平均电流降至0.65 mA，相当于在单节2,500 mAh电池上估计可无人值守运行159天。结果表明，低成本边缘—云架构无需服务器级硬件即可为微精准农业提供可靠、及时且能源可持续的灌溉控制。","World Academics Journal of Engineering Sciences","2026-09-30T00:00:00Z",{"impact":19,"substance":121,"depth":17,"authority":235,"freshness":85,"relevant":22,"comment":443},"低成本边缘-云架构实现植物胁迫自动诊断与灌溉闭环，数据扎实、结论可靠，对智慧农业节水灌溉有参考价值。",[445],{"name":440,"url":437},[126,27,28,128,447],"土壤墒情",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[450,451],{"code":78,"slug":132,"name":133,"path":134,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":453},{"candidates":454,"agricultural_evidence":58},[455,459,463,468,472,476],{"code":46,"score":17,"supported":58,"title":456,"abstract":457,"tags":458},[66],[68,69,66],[28],{"code":78,"score":72,"supported":58,"title":460,"abstract":461,"tags":462},[147],[146,147],[27],{"code":33,"score":13,"supported":58,"title":464,"abstract":465,"tags":467},[],[61,34,466],"evapotranspiration",[128],{"code":406,"score":155,"supported":58,"title":469,"abstract":470,"tags":471},[],[409,410],[447],{"code":154,"score":155,"supported":58,"title":473,"abstract":474,"tags":475},[],[395,393],[126],{"code":263,"score":85,"supported":58,"title":477,"abstract":478,"tags":480},[],[266,479],"smallholder",[],[482,483],"ESP32 土壤湿度 灌溉 机器学习","随机森林 植物胁迫 诊断","ESP32土壤湿度灌溉机器学习-4076",{"doi":9,"openalex_id":486,"authors":487,"venue":440,"cited_by_count":95,"oa_url":437,"card":494,"direction":106,"ingested_from":108},"W7217836036",[488,490,492],{"name":489,"orcid":9},"Christian Athanas Mwakyusa",{"name":491,"orcid":9},"Allen Deogratius Mwalekwa",{"name":493,"orcid":9},"Gift Geofrey Mushi",{"tldr":495,"method":496,"finding":497,"direction":106,"opportunity":498},"设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。","ESP32、电容式土壤湿度传感器、DHT22、随机森林分类器、Flask云服务。","48小时测试分类准确率96.8%，脱水胁迫召回率97.9%，节点续航约159天。","可探索多作物泛化、极端环境鲁棒性及边缘端轻量化模型部署以降低云依赖。","2026-10-03T23:30:18.857006Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":506,"source_url":503,"published_at":507,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":508,"score_detail":509,"sources":511,"tags":513,"primary_topic":517,"secondary_topics":518,"classification":521,"search_phrases":539,"slug":542,"view_count":543,"doi":544,"paper":545,"created_at":568},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",74,{"impact":72,"substance":121,"depth":17,"authority":13,"freshness":21,"relevant":22,"comment":510},"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[512],{"name":506,"url":503},[27,514,29,515,516],"Sentinel-2","水资源管理","地表水体提取",{"code":78,"slug":132,"name":133,"path":134,"parent_code":44},[519,520],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":522},{"candidates":523,"agricultural_evidence":58},[524,530,535],{"code":78,"score":72,"supported":58,"title":525,"abstract":527,"tags":529},[526],"deep learning",[528,526],"深度学习",[27],{"code":33,"score":13,"supported":58,"title":531,"abstract":532,"tags":534},[],[61,62,34,533],"water resource",[515],{"code":40,"score":311,"supported":58,"title":536,"abstract":537,"tags":538},[],[29,74],[514,29],[540,541],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504",2,"10.24850\u002Fj-tyca-18-03-08",{"doi":544,"openalex_id":546,"authors":547,"venue":506,"cited_by_count":95,"oa_url":503,"card":563,"direction":220,"ingested_from":108},"W7214228825",[548,551,554,557,560],{"name":549,"orcid":550},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":552,"orcid":553},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":555,"orcid":556},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":558,"orcid":559},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":561,"orcid":562},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":564,"method":565,"finding":566,"direction":220,"opportunity":567},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z"]