[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4671":3,"related-4671":117},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":47,"search_phrases":92,"slug":95,"view_count":22,"doi":9,"paper":96,"created_at":116},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。",null,"International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z","论文",25,false,53,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,14,13,6,8,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[29,30,31,32,33],"智慧农业","农业人工智能","农业物联网","边缘计算","精准灌溉",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[41],{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":48,"locked":14,"version":49,"method":50,"evidence":51},"classified","2026-10-22.2","rules",{"candidates":52,"agricultural_evidence":55},[53,62,70,77,85],{"code":35,"score":54,"supported":55,"title":56,"abstract":58,"tags":61},19,true,[57],"IoT",[59,60,57],"物联网","internet of things",[31,32],{"code":63,"score":64,"supported":55,"title":65,"abstract":67,"tags":69},"I1",15,[66],"precision agriculture",[68,66],"精准农业",[29],{"code":42,"score":71,"supported":55,"title":72,"abstract":73,"tags":76},10,[],[74,75,43],"灌溉","水资源",[33],{"code":78,"score":79,"supported":55,"title":80,"abstract":81,"tags":84},"I2",7,[],[82,83],"机器学习","machine learning",[30],{"code":86,"score":20,"supported":55,"title":87,"abstract":88,"tags":91},"H2",[],[89,90],"土壤","soil",[],[93,94],"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":97,"authors":98,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7221411136",[99,101,103,105],{"name":100,"orcid":9},"Om Gaikwad",{"name":102,"orcid":9},"Shriraj Chavan",{"name":104,"orcid":9},"Krushnadev Gaikwad",{"name":106,"orcid":107},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-11T23:30:16.298334Z",{"total":20,"page":22,"page_size":20,"items":118},[119,198,270,375,444,522],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":133,"tags":137,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":175,"slug":178,"view_count":108,"doi":179,"paper":180,"created_at":197},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",73,{"impact":129,"substance":130,"depth":131,"authority":19,"freshness":108,"relevant":22,"comment":132},22,21,17,"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。",[134,135],{"name":125,"url":122},{"name":125,"url":136},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[29,30,31,138,33],"区块链溯源",{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},[141],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":48,"locked":14,"version":49,"method":50,"evidence":143},{"candidates":144,"agricultural_evidence":55},[145,149,153,159,166,171],{"code":42,"score":64,"supported":55,"title":146,"abstract":147,"tags":148},[43],[74,43],[33],{"code":35,"score":71,"supported":55,"title":150,"abstract":151,"tags":152},[],[59,60,57],[31],{"code":78,"score":154,"supported":55,"title":155,"abstract":156,"tags":158},4,[],[157],"artificial intelligence",[30],{"code":160,"score":161,"supported":55,"title":162,"abstract":163,"tags":165},"G2",3,[],[164],"小农户",[],{"code":167,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},"E4",[],[],[138],{"code":63,"score":22,"supported":14,"title":172,"abstract":173,"tags":174},[],[],[29],[176,177],"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":179,"openalex_id":181,"authors":182,"venue":125,"cited_by_count":108,"oa_url":122,"card":191,"direction":113,"ingested_from":115},"W7223034914",[183,186,188],{"name":184,"orcid":185},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":187,"orcid":9},"Dr.T.Baskar",{"name":189,"orcid":190},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"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":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":71,"is_selected":14,"score":206,"score_detail":207,"sources":211,"tags":213,"primary_topic":215,"secondary_topics":216,"classification":222,"search_phrases":251,"slug":254,"view_count":154,"doi":9,"paper":255,"created_at":269},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",75,{"impact":208,"substance":129,"depth":209,"authority":19,"freshness":20,"relevant":22,"comment":210},16,18,"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。",[212],{"name":204,"url":201},[29,30,31,33,214],"土壤墒情",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[217,221],{"code":78,"slug":218,"name":219,"path":220,"parent_code":39},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},{"status":48,"locked":14,"version":49,"method":50,"evidence":223},{"candidates":224,"agricultural_evidence":55},[225,229,233,238,242,246],{"code":35,"score":209,"supported":55,"title":226,"abstract":227,"tags":228},[57],[59,60,57],[31],{"code":78,"score":64,"supported":55,"title":230,"abstract":231,"tags":232},[83],[82,83],[30],{"code":42,"score":71,"supported":55,"title":234,"abstract":235,"tags":237},[],[74,43,236],"evapotranspiration",[33],{"code":86,"score":79,"supported":55,"title":239,"abstract":240,"tags":241},[],[89,90],[214],{"code":63,"score":79,"supported":55,"title":243,"abstract":244,"tags":245},[],[68,66],[29],{"code":160,"score":20,"supported":55,"title":247,"abstract":248,"tags":250},[],[164,249],"smallholder",[],[252,253],"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":256,"authors":257,"venue":204,"cited_by_count":108,"oa_url":201,"card":264,"direction":113,"ingested_from":115},"W7217836036",[258,260,262],{"name":259,"orcid":9},"Christian Athanas Mwakyusa",{"name":261,"orcid":9},"Allen Deogratius Mwalekwa",{"name":263,"orcid":9},"Gift Geofrey Mushi",{"tldr":265,"method":266,"finding":267,"direction":113,"opportunity":268},"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":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":71,"is_selected":14,"score":278,"score_detail":279,"sources":282,"tags":284,"primary_topic":287,"secondary_topics":288,"classification":291,"search_phrases":345,"slug":348,"view_count":108,"doi":349,"paper":350,"created_at":374},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":209,"substance":129,"depth":209,"authority":17,"freshness":280,"relevant":22,"comment":281},9,"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。",[283],{"name":276,"url":273},[29,30,285,286,33],"遥感","作物水分胁迫",{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},[289,290],{"code":78,"slug":218,"name":219,"path":220,"parent_code":39},{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":48,"locked":14,"version":49,"method":50,"evidence":292},{"candidates":293,"agricultural_evidence":55},[294,298,302,306,312,318,325,332,339],{"code":42,"score":64,"supported":55,"title":295,"abstract":296,"tags":297},[43],[74,43],[33],{"code":78,"score":71,"supported":55,"title":299,"abstract":300,"tags":301},[],[82,83,157],[30],{"code":35,"score":280,"supported":55,"title":303,"abstract":304,"tags":305},[],[59,60,57],[],{"code":63,"score":79,"supported":55,"title":307,"abstract":308,"tags":311},[],[309,310],"数字孪生","digital twin",[29],{"code":313,"score":79,"supported":55,"title":314,"abstract":315,"tags":317},"I3",[],[285,316],"remote sensing",[285],{"code":319,"score":20,"supported":55,"title":320,"abstract":321,"tags":324},"A2",[],[322,323],"小麦","wheat",[],{"code":326,"score":20,"supported":55,"title":327,"abstract":328,"tags":331},"A3",[],[329,330],"玉米","maize",[],{"code":333,"score":20,"supported":55,"title":334,"abstract":335,"tags":338},"A5",[],[336,337],"马铃薯","potato",[],{"code":340,"score":161,"supported":55,"title":341,"abstract":342,"tags":344},"B2",[],[343],"葡萄",[],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":276,"cited_by_count":108,"oa_url":273,"card":368,"direction":113,"ingested_from":115},"W7223088921",[353,356,359,362,365],{"name":354,"orcid":355},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":357,"orcid":358},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":360,"orcid":361},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":363,"orcid":364},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":366,"orcid":367},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":369,"method":370,"finding":371,"direction":372,"opportunity":373},"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":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":9,"source_name":381,"source_url":378,"published_at":382,"category":12,"cover_url":9,"hotness":71,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":390,"secondary_topics":391,"classification":393,"search_phrases":423,"slug":426,"view_count":427,"doi":428,"paper":429,"created_at":443},4561,"Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajese.20261004.11","This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method\u002Farchitecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resources, and the cost of farm inputs, offering an incentive to transition to data-driven and sensor-based farm management practices. Internet of Things (IoT)-based agricultural monitoring is well developed, but most systems deployed focus on a single parameter (e.g., soil moisture) or the parameter is inferred solely in the cloud, adding latency and fragility to the deployment of systems in rural areas. In this study, we propose an integrated architecture for low-power multiparameter environmental sensing, LoRaWAN\u002FNB-IoT connectivity, edge-based lightweight inference, and privacy-preserving federated learning with cross-farm model aggregation, which is further complemented by a digital twin layer for scenario simulation. The decision layer of the framework can combine data from ground sensors with UAV and satellite images for the multi-scale monitoring of crop health and environmental quality (air, water, and GHG proxies). We contribute the full system design, an algorithmic pipeline, a sensor and model configuration specification, and a pre-registered evaluation protocol intended to guide future field deployment; no field data have yet been collected and clearly labelled non-empirical templates showing the expected reporting format only, rather than measured outcomes. The architecture and protocol are instead grounded in, and cross-checked against, published state-of-the-art results, which report an accuracy range of 82–97% for federated crop-yield prediction and 20–30% water savings from sensor-controlled irrigation.","本研究提出了一套面向智慧农业与环境监测的系统架构、算法与评估协议。这是一项方法\u002F架构层面的贡献，不报告田间试验结果。世界粮食生产仍面临气候变率、淡水资源可得性以及农业投入成本带来的压力，这促使农业管理实践向数据驱动和基于传感器的方向转型。基于物联网（IoT）的农业监测已较为成熟，但大多数已部署系统仅关注单一参数（如土壤水分），或仅在云端推断参数，这增加了农村地区系统部署的延迟和脆弱性。在本研究中，我们提出了一种集成架构，涵盖低功耗多参数环境感知、LoRaWAN\u002FNB-IoT连接、基于边缘的轻量级推理，以及具有跨农场模型聚合的隐私保护联邦学习，并进一步辅以用于情景模拟的数字孪生层。该框架的决策层可将地面传感器数据与无人机和卫星图像相结合，用于作物健康和环境质量（空气、水和温室气体代理指标）的多尺度监测。我们贡献了完整的系统设计、算法流程、传感器与模型配置规范，以及一份预注册的评估协议，旨在指导未来的田间部署；目前尚未收集田间数据，并明确标注了非经验性模板，仅展示预期报告格式，而非实测结果。该架构和协议以已发表的最先进成果为依据并与之交叉验证，这些成果报告联邦作物产量预测的准确率范围为82–97%，传感器控制灌溉可节水20–30%。","American Journal of Environmental Science and Engineering","2026-10-09T00:00:00Z",66,{"impact":17,"substance":208,"depth":131,"authority":19,"freshness":21,"relevant":22,"comment":385},"提出边缘AI+IoT+联邦学习的抗气候风险农业监测架构与评估协议，方法设计完整但尚无田间实测数据，属有参考价值的预研性论文。",[387],{"name":381,"url":378},[29,31,32,309,389],"联邦学习",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[392],{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},{"status":48,"locked":14,"version":49,"method":50,"evidence":394},{"candidates":395,"agricultural_evidence":55},[396,401,405,409,413,419],{"code":63,"score":54,"supported":55,"title":397,"abstract":399,"tags":400},[398],"smart agriculture",[29,309,398,310],[309,29],{"code":35,"score":54,"supported":55,"title":402,"abstract":403,"tags":404},[57],[59,60,57],[31,32],{"code":42,"score":280,"supported":55,"title":406,"abstract":407,"tags":408},[],[74,75,43],[],{"code":86,"score":20,"supported":55,"title":410,"abstract":411,"tags":412},[],[89,90],[],{"code":414,"score":161,"supported":55,"title":415,"abstract":416,"tags":418},"B7",[],[417],"温室",[],{"code":78,"score":22,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[389],[424,425],"LoRaWAN NB-IoT 农业监测","联邦学习 作物产量预测","LoRaWANNB-IoT农业监测-4561",2,"10.11648\u002Fj.ajese.20261004.11",{"doi":428,"openalex_id":430,"authors":431,"venue":381,"cited_by_count":108,"oa_url":378,"card":438,"direction":113,"ingested_from":115},"W7221105407",[432,435],{"name":433,"orcid":434},"Zarin Progga","https:\u002F\u002Forcid.org\u002F0009-0004-2311-1210",{"name":436,"orcid":437},"Mohammad Ali","https:\u002F\u002Forcid.org\u002F0009-0009-3725-8535",{"tldr":439,"method":440,"finding":441,"direction":113,"opportunity":442},"提出边缘AI、物联网与联邦学习集成的智慧农业环境监测架构与评估协议，未开展实地试验。","LoRaWAN\u002FNB-IoT多参数传感、边缘轻量推理、跨农场联邦学习、数字孪生仿","架构基于已发表结果交叉验证，联邦产量预测精度82-97%，传感器灌溉节水20-30%。","该框架仅停留在设计阶段，未来可开展真实农田部署，验证边缘联邦学习在异构农场环境下的精度与通信开销。","2026-10-10T23:30:07.789940Z",{"id":445,"title":446,"url":447,"summary":448,"summary_zh":9,"content":9,"source_name":449,"source_url":447,"published_at":450,"category":12,"cover_url":9,"hotness":451,"is_selected":14,"score":452,"score_detail":453,"sources":455,"tags":464,"primary_topic":466,"secondary_topics":467,"classification":470,"search_phrases":501,"slug":504,"view_count":154,"doi":505,"paper":506,"created_at":521},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",55,74,{"impact":209,"substance":129,"depth":209,"authority":19,"freshness":161,"relevant":22,"comment":454},"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[456,457,459,461],{"name":449,"url":447},{"name":125,"url":458},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":125,"url":460},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":462,"url":463},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[29,30,32,59,465],"智能灌溉",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[468,469],{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},{"code":78,"slug":218,"name":219,"path":220,"parent_code":39},{"status":48,"locked":14,"version":49,"method":50,"evidence":471},{"candidates":472,"agricultural_evidence":55},[473,479,483,487,493,497],{"code":35,"score":474,"supported":55,"title":475,"abstract":477,"tags":478},27,[57,476],"edge computing",[60,57,476],[59,32],{"code":42,"score":17,"supported":55,"title":480,"abstract":481,"tags":482},[43],[43],[465],{"code":78,"score":17,"supported":55,"title":484,"abstract":485,"tags":486},[83],[83],[30],{"code":488,"score":161,"supported":55,"title":489,"abstract":490,"tags":492},"A8",[],[491],"food security",[],{"code":86,"score":161,"supported":55,"title":494,"abstract":495,"tags":496},[],[90],[],{"code":63,"score":22,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[29],[502,503],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":505,"openalex_id":507,"authors":508,"venue":449,"cited_by_count":108,"oa_url":515,"card":516,"direction":113,"ingested_from":115},"W7204189894",[509,512],{"name":510,"orcid":511},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":513,"orcid":514},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":517,"method":518,"finding":519,"direction":113,"opportunity":520},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":523,"title":524,"url":525,"summary":526,"summary_zh":527,"content":9,"source_name":528,"source_url":525,"published_at":277,"category":12,"cover_url":9,"hotness":71,"is_selected":14,"score":529,"score_detail":530,"sources":532,"tags":534,"primary_topic":536,"secondary_topics":537,"classification":540,"search_phrases":586,"slug":589,"view_count":108,"doi":590,"paper":591,"created_at":610},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":209,"substance":129,"depth":209,"authority":19,"freshness":280,"relevant":22,"comment":531},"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。",[533],{"name":528,"url":525},[29,30,59,68,535],"气候韧性",{"code":78,"slug":218,"name":219,"path":220,"parent_code":39},[538,539],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":42,"slug":43,"name":44,"path":45,"parent_code":46},{"status":48,"locked":14,"version":49,"method":50,"evidence":541},{"candidates":542,"agricultural_evidence":55},[543,551,555,559,563,570,574,580],{"code":78,"score":209,"supported":55,"title":544,"abstract":545,"tags":550},[157],[546,82,547,83,548,549],"深度学习","计算机视觉","deep learning","computer vision",[30],{"code":63,"score":208,"supported":55,"title":552,"abstract":553,"tags":554},[66],[68,66],[29,68],{"code":35,"score":71,"supported":55,"title":556,"abstract":557,"tags":558},[],[59,32,57,476],[59],{"code":42,"score":280,"supported":55,"title":560,"abstract":561,"tags":562},[],[74,75,43],[],{"code":564,"score":79,"supported":55,"title":565,"abstract":566,"tags":569},"H7",[],[567,568],"气候变化","climate change",[535],{"code":86,"score":20,"supported":55,"title":571,"abstract":572,"tags":573},[],[89,90],[],{"code":575,"score":161,"supported":55,"title":576,"abstract":577,"tags":579},"A7",[],[578],"间作",[],{"code":581,"score":161,"supported":55,"title":582,"abstract":583,"tags":585},"F3",[],[584],"养分管理",[],[587,588],"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":590,"openalex_id":592,"authors":593,"venue":528,"cited_by_count":108,"oa_url":525,"card":604,"direction":113,"ingested_from":115},"W7223121456",[594,597,599,601],{"name":595,"orcid":596},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":598,"orcid":9},"Oluwadare OYEBODE",{"name":600,"orcid":9},"Iretiayo Adelaiye",{"name":602,"orcid":603},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":605,"method":606,"finding":607,"direction":608,"opportunity":609},"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"]