[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2312":3,"related-2312":116},{"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":35,"secondary_topics":41,"classification":42,"search_phrases":96,"slug":99,"view_count":100,"doi":101,"paper":102,"created_at":115},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z","论文",25,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,13,9,1,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[29,30,31,32,33,34],"数字乡村","智慧农业","农业人工智能","农业物联网","小农户","精准灌溉",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,58,63,71,78,84,91],{"code":49,"score":19,"supported":50,"title":51,"abstract":53,"tags":57},"I1",true,[52],"precision agriculture",[54,55,52,56],"精准农业","数字农业","digital agriculture",[30],{"code":36,"score":21,"supported":50,"title":59,"abstract":61,"tags":62},[60],"IoT",[],[32],{"code":64,"score":65,"supported":50,"title":66,"abstract":67,"tags":70},"I2",7,[],[68,69],"artificial intelligence","large language model",[31],{"code":72,"score":73,"supported":50,"title":74,"abstract":75,"tags":77},"G2",4,[],[76],"smallholder",[33],{"code":79,"score":73,"supported":50,"title":80,"abstract":81,"tags":83},"H3",[],[82],"evapotranspiration",[34],{"code":85,"score":86,"supported":50,"title":87,"abstract":88,"tags":90},"H2",3,[],[89],"土壤",[],{"code":92,"score":22,"supported":14,"title":93,"abstract":94,"tags":95},"I6",[],[],[29],[97,98],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312",2,"10.5281\u002Fzenodo.22724805",{"doi":101,"openalex_id":103,"authors":104,"venue":10,"cited_by_count":107,"oa_url":6,"card":108,"direction":112,"ingested_from":114},"W7212357737",[105],{"name":106,"orcid":9},"Pranit Kamble",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","智慧农业 \u002F 农业物联网","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","openalex","2026-09-13T23:30:14.323767Z",{"total":117,"page":22,"page_size":117,"items":118},6,[119,209,285,355,424,527],{"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":127,"is_selected":14,"score":128,"score_detail":129,"sources":132,"tags":134,"primary_topic":135,"secondary_topics":141,"classification":148,"search_phrases":189,"slug":192,"view_count":100,"doi":193,"paper":194,"created_at":208},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",10,79,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":130,"relevant":22,"comment":131},8,"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[133],{"name":125,"url":122},[30,31,32,33,34],{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[142,143],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":72,"slug":144,"name":145,"path":146,"parent_code":147},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":43,"locked":14,"version":44,"method":45,"evidence":149},{"candidates":150,"agricultural_evidence":50},[151,159,165,170,175,180,185],{"code":136,"score":152,"supported":50,"title":153,"abstract":155,"tags":158},17,[154],"fertigation",[156,157,154],"施肥","水肥一体化",[],{"code":36,"score":160,"supported":50,"title":161,"abstract":162,"tags":164},15,[60],[163,60],"物联网",[32],{"code":72,"score":166,"supported":50,"title":167,"abstract":168,"tags":169},12,[76],[76],[33],{"code":64,"score":21,"supported":50,"title":171,"abstract":173,"tags":174},[172],"decision support",[],[31],{"code":85,"score":117,"supported":50,"title":176,"abstract":177,"tags":179},[],[89,178],"soil",[],{"code":79,"score":73,"supported":50,"title":181,"abstract":182,"tags":184},[],[183],"灌溉",[34],{"code":49,"score":22,"supported":14,"title":186,"abstract":187,"tags":188},[],[],[30],[190,191],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010","10.5120\u002Fijca9a6e56b81235",{"doi":193,"openalex_id":195,"authors":196,"venue":125,"cited_by_count":107,"oa_url":122,"card":203,"direction":112,"ingested_from":114},"W7213663876",[197,199,201],{"name":198,"orcid":9},"Awojide S.",{"name":200,"orcid":9},"Ikpotokin F.O.",{"name":202,"orcid":9},"Sadiq F.I.",{"tldr":204,"method":205,"finding":206,"direction":112,"opportunity":207},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":10,"source_url":212,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":224,"primary_topic":226,"secondary_topics":231,"classification":233,"search_phrases":262,"slug":265,"view_count":107,"doi":266,"paper":267,"created_at":284},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",73,{"impact":17,"substance":218,"depth":152,"authority":20,"freshness":107,"relevant":22,"comment":219},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。",[221,222],{"name":10,"url":212},{"name":10,"url":223},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[30,31,32,225,34],"区块链溯源",{"code":79,"slug":227,"name":228,"path":229,"parent_code":230},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[232],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":234},{"candidates":235,"agricultural_evidence":50},[236,240,245,249,253,258],{"code":79,"score":160,"supported":50,"title":237,"abstract":238,"tags":239},[227],[183,227],[34],{"code":36,"score":127,"supported":50,"title":241,"abstract":242,"tags":244},[],[163,243,60],"internet of things",[32],{"code":64,"score":73,"supported":50,"title":246,"abstract":247,"tags":248},[],[68],[31],{"code":72,"score":86,"supported":50,"title":250,"abstract":251,"tags":252},[],[33],[],{"code":254,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},"E4",[],[],[225],{"code":49,"score":22,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[30],[263,264],"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":266,"openalex_id":268,"authors":269,"venue":10,"cited_by_count":107,"oa_url":212,"card":278,"direction":112,"ingested_from":114},"W7223034914",[270,273,275],{"name":271,"orcid":272},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":274,"orcid":9},"Dr.T.Baskar",{"name":276,"orcid":277},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":279,"method":280,"finding":281,"direction":282,"opportunity":283},"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":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":291,"source_url":288,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":297,"tags":301,"primary_topic":303,"secondary_topics":304,"classification":306,"search_phrases":333,"slug":336,"view_count":22,"doi":9,"paper":337,"created_at":354},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":166,"substance":295,"depth":20,"authority":117,"freshness":130,"relevant":22,"comment":296},14,"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。",[298,299],{"name":291,"url":288},{"name":291,"url":300},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[30,31,32,302,34],"边缘计算",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[305],{"code":79,"slug":227,"name":228,"path":229,"parent_code":230},{"status":43,"locked":14,"version":44,"method":45,"evidence":307},{"candidates":308,"agricultural_evidence":50},[309,314,318,323,329],{"code":36,"score":310,"supported":50,"title":311,"abstract":312,"tags":313},19,[60],[163,243,60],[32,302],{"code":49,"score":160,"supported":50,"title":315,"abstract":316,"tags":317},[52],[54,52],[30],{"code":79,"score":127,"supported":50,"title":319,"abstract":320,"tags":322},[],[183,321,227],"水资源",[34],{"code":64,"score":65,"supported":50,"title":324,"abstract":325,"tags":328},[],[326,327],"机器学习","machine learning",[31],{"code":85,"score":117,"supported":50,"title":330,"abstract":331,"tags":332},[],[89,178],[],[334,335],"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":338,"authors":339,"venue":291,"cited_by_count":107,"oa_url":288,"card":349,"direction":112,"ingested_from":114},"W7221411136",[340,342,344,346],{"name":341,"orcid":9},"Om Gaikwad",{"name":343,"orcid":9},"Shriraj Chavan",{"name":345,"orcid":9},"Krushnadev Gaikwad",{"name":347,"orcid":348},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":350,"method":351,"finding":352,"direction":112,"opportunity":353},"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":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":361,"source_url":358,"published_at":362,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":363,"score_detail":364,"sources":367,"tags":369,"primary_topic":371,"secondary_topics":372,"classification":378,"search_phrases":405,"slug":408,"view_count":73,"doi":9,"paper":409,"created_at":423},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":365,"substance":17,"depth":19,"authority":20,"freshness":117,"relevant":22,"comment":366},16,"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。",[368],{"name":361,"url":358},[30,31,32,34,370],"土壤墒情",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[373,377],{"code":64,"slug":374,"name":375,"path":376,"parent_code":40},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":79,"slug":227,"name":228,"path":229,"parent_code":230},{"status":43,"locked":14,"version":44,"method":45,"evidence":379},{"candidates":380,"agricultural_evidence":50},[381,385,389,393,397,401],{"code":36,"score":19,"supported":50,"title":382,"abstract":383,"tags":384},[60],[163,243,60],[32],{"code":64,"score":160,"supported":50,"title":386,"abstract":387,"tags":388},[327],[326,327],[31],{"code":79,"score":127,"supported":50,"title":390,"abstract":391,"tags":392},[],[183,227,82],[34],{"code":85,"score":65,"supported":50,"title":394,"abstract":395,"tags":396},[],[89,178],[370],{"code":49,"score":65,"supported":50,"title":398,"abstract":399,"tags":400},[],[54,52],[30],{"code":72,"score":117,"supported":50,"title":402,"abstract":403,"tags":404},[],[33,76],[],[406,407],"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":410,"authors":411,"venue":361,"cited_by_count":107,"oa_url":358,"card":418,"direction":112,"ingested_from":114},"W7217836036",[412,414,416],{"name":413,"orcid":9},"Christian Athanas Mwakyusa",{"name":415,"orcid":9},"Allen Deogratius Mwalekwa",{"name":417,"orcid":9},"Gift Geofrey Mushi",{"tldr":419,"method":420,"finding":421,"direction":112,"opportunity":422},"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":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":431,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":128,"score_detail":432,"sources":434,"tags":436,"primary_topic":439,"secondary_topics":440,"classification":443,"search_phrases":497,"slug":500,"view_count":107,"doi":501,"paper":502,"created_at":526},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",{"impact":19,"substance":17,"depth":19,"authority":166,"freshness":21,"relevant":22,"comment":433},"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。",[435],{"name":430,"url":427},[30,31,437,438,34],"遥感","作物水分胁迫",{"code":79,"slug":227,"name":228,"path":229,"parent_code":230},[441,442],{"code":64,"slug":374,"name":375,"path":376,"parent_code":40},{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":444},{"candidates":445,"agricultural_evidence":50},[446,450,454,458,464,470,477,484,491],{"code":79,"score":160,"supported":50,"title":447,"abstract":448,"tags":449},[227],[183,227],[34],{"code":64,"score":127,"supported":50,"title":451,"abstract":452,"tags":453},[],[326,327,68],[31],{"code":36,"score":21,"supported":50,"title":455,"abstract":456,"tags":457},[],[163,243,60],[],{"code":49,"score":65,"supported":50,"title":459,"abstract":460,"tags":463},[],[461,462],"数字孪生","digital twin",[30],{"code":465,"score":65,"supported":50,"title":466,"abstract":467,"tags":469},"I3",[],[437,468],"remote sensing",[437],{"code":471,"score":117,"supported":50,"title":472,"abstract":473,"tags":476},"A2",[],[474,475],"小麦","wheat",[],{"code":478,"score":117,"supported":50,"title":479,"abstract":480,"tags":483},"A3",[],[481,482],"玉米","maize",[],{"code":485,"score":117,"supported":50,"title":486,"abstract":487,"tags":490},"A5",[],[488,489],"马铃薯","potato",[],{"code":492,"score":86,"supported":50,"title":493,"abstract":494,"tags":496},"B2",[],[495],"葡萄",[],[498,499],"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":501,"openalex_id":503,"authors":504,"venue":430,"cited_by_count":107,"oa_url":427,"card":520,"direction":112,"ingested_from":114},"W7223088921",[505,508,511,514,517],{"name":506,"orcid":507},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":509,"orcid":510},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":512,"orcid":513},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":515,"orcid":516},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":518,"orcid":519},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":521,"method":522,"finding":523,"direction":524,"opportunity":525},"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":528,"title":529,"url":530,"summary":531,"summary_zh":532,"content":9,"source_name":533,"source_url":530,"published_at":534,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":535,"score_detail":536,"sources":538,"tags":540,"primary_topic":543,"secondary_topics":544,"classification":546,"search_phrases":562,"slug":565,"view_count":107,"doi":566,"paper":567,"created_at":587},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":365,"substance":218,"depth":19,"authority":166,"freshness":21,"relevant":22,"comment":537},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[539],{"name":533,"url":530},[30,31,32,541,542],"大语言模型","传感器标准化",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[545],{"code":64,"slug":374,"name":375,"path":376,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":547},{"candidates":548,"agricultural_evidence":50},[549,553,557],{"code":36,"score":19,"supported":50,"title":550,"abstract":551,"tags":552},[60],[163,243,60],[32],{"code":64,"score":365,"supported":50,"title":554,"abstract":555,"tags":556},[172],[69,172],[31,541],{"code":49,"score":65,"supported":50,"title":558,"abstract":559,"tags":561},[],[30,560],"smart agriculture",[30],[563,564],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":566,"openalex_id":568,"authors":569,"venue":533,"cited_by_count":107,"oa_url":530,"card":582,"direction":112,"ingested_from":114},"W7222920424",[570,572,574,576,578,580],{"name":571,"orcid":9},"Leqi Lai",{"name":573,"orcid":9},"Langtao Duan",{"name":575,"orcid":9},"Yuhao Chen",{"name":577,"orcid":9},"Yuhang Zheng",{"name":579,"orcid":9},"Jinyu Yang",{"name":581,"orcid":9},"Mingyao Wen",{"tldr":583,"method":584,"finding":585,"direction":112,"opportunity":586},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z"]