[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3979":3,"related-3979":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":96},3979,"From sensing to modeling: machine learning-driven wearable humidity sensors for plant water status monitoring","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112490","From sensing to modeling: machine learning-driven wearable humidity sensors for plant water status monitoring。Computers and Electronics in Agriculture","从传感走向建模：机器学习驱动的可穿戴湿度传感器用于植物水分状态监测",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"核心期刊论文，将可穿戴湿度传感与机器学习建模结合用于作物水分状态监测，方法新颖、对精准灌溉有参考价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准灌溉","可穿戴传感器","作物水分监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,57,62],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},15,true,[48],"machine learning",[50,48],"机器学习",[27],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"H3",[],[],[28],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I5",[],[],[29],[68,69],"可穿戴湿度传感器 作物水分监测","机器学习 植物水分状态","可穿戴湿度传感器作物水分监测-3979",3,"10.1016\u002Fj.compag.2026.112490",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":88,"oa_url":9,"card":89,"direction":93,"ingested_from":95},"W7215113264",[76,78,80,82,85],{"name":77,"orcid":9},"Xiangzeng Kong",{"name":79,"orcid":9},"Jinrong Lin",{"name":81,"orcid":9},"Wangxiao Wen",{"name":83,"orcid":84},"Shihan Liu","https:\u002F\u002Forcid.org\u002F0000-0002-5780-5673",{"name":86,"orcid":87},"Qichang Hu","https:\u002F\u002Forcid.org\u002F0000-0003-2672-5862",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"将可穿戴湿度传感器与机器学习结合，实现植物水分状态监测。","可穿戴湿度传感器采集数据，机器学习建模分析植物水分状态。","机器学习驱动的可穿戴传感器能有效监测植物水分状态。","其他","可探索多模态传感器融合与轻量化模型，实现田间实时精准灌溉决策。","openalex","2026-10-02T23:30:03.194855Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,221,294,367,437,518],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":114,"tags":116,"primary_topic":119,"secondary_topics":124,"classification":130,"search_phrases":190,"slug":193,"view_count":88,"doi":194,"paper":195,"created_at":220},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":17,"substance":110,"depth":17,"authority":111,"freshness":112,"relevant":21,"comment":113},22,12,9,"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。",[115],{"name":106,"url":103},[26,27,117,118,28],"遥感","作物水分胁迫",{"code":53,"slug":120,"name":121,"path":122,"parent_code":123},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[125,126],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":63,"slug":127,"name":128,"path":129,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":131},{"candidates":132,"agricultural_evidence":46},[133,138,143,150,157,163,170,177,184],{"code":53,"score":45,"supported":46,"title":134,"abstract":135,"tags":137},[120],[136,120],"灌溉",[28],{"code":32,"score":13,"supported":46,"title":139,"abstract":140,"tags":142},[],[50,48,141],"artificial intelligence",[27],{"code":63,"score":112,"supported":46,"title":144,"abstract":145,"tags":149},[],[146,147,148],"物联网","internet of things","IoT",[],{"code":58,"score":151,"supported":46,"title":152,"abstract":153,"tags":156},7,[],[154,155],"数字孪生","digital twin",[26],{"code":158,"score":151,"supported":46,"title":159,"abstract":160,"tags":162},"I3",[],[117,161],"remote sensing",[117],{"code":164,"score":98,"supported":46,"title":165,"abstract":166,"tags":169},"A2",[],[167,168],"小麦","wheat",[],{"code":171,"score":98,"supported":46,"title":172,"abstract":173,"tags":176},"A3",[],[174,175],"玉米","maize",[],{"code":178,"score":98,"supported":46,"title":179,"abstract":180,"tags":183},"A5",[],[181,182],"马铃薯","potato",[],{"code":185,"score":71,"supported":46,"title":186,"abstract":187,"tags":189},"B2",[],[188],"葡萄",[],[191,192],"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":194,"openalex_id":196,"authors":197,"venue":106,"cited_by_count":88,"oa_url":103,"card":213,"direction":219,"ingested_from":95},"W7223088921",[198,201,204,207,210],{"name":199,"orcid":200},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":202,"orcid":203},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":205,"orcid":206},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":208,"orcid":209},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":211,"orcid":212},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":232,"tags":234,"primary_topic":236,"secondary_topics":237,"classification":239,"search_phrases":273,"slug":276,"view_count":21,"doi":277,"paper":278,"created_at":293},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":20,"substance":17,"depth":45,"authority":98,"freshness":20,"relevant":21,"comment":231},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[233],{"name":227,"url":224},[26,27,235,146,28],"边缘计算",{"code":63,"slug":127,"name":128,"path":129,"parent_code":36},[238],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":240},{"candidates":241,"agricultural_evidence":46},[242,247,256,260,266],{"code":63,"score":243,"supported":46,"title":244,"abstract":245,"tags":246},11,[],[146,147,148],[146,235],{"code":32,"score":13,"supported":46,"title":248,"abstract":249,"tags":255},[],[250,251,252,253,254],"深度学习","计算机视觉","deep learning","computer vision","decision support",[27],{"code":53,"score":151,"supported":46,"title":257,"abstract":258,"tags":259},[],[136,120],[28],{"code":58,"score":151,"supported":46,"title":261,"abstract":262,"tags":265},[],[263,264],"精准农业","precision agriculture",[26],{"code":267,"score":98,"supported":46,"title":268,"abstract":269,"tags":272},"H2",[],[270,271],"土壤","soil",[],[274,275],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":277,"openalex_id":279,"authors":280,"venue":227,"cited_by_count":88,"oa_url":224,"card":288,"direction":219,"ingested_from":95},"W7220855887",[281,284,286],{"name":282,"orcid":283},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":285,"orcid":9},"Shetty Chirag",{"name":287,"orcid":9},"Sunay Kanade",{"tldr":289,"method":290,"finding":291,"direction":219,"opportunity":292},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":307,"tags":309,"primary_topic":312,"secondary_topics":313,"classification":316,"search_phrases":347,"slug":350,"view_count":351,"doi":9,"paper":352,"created_at":366},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":304,"substance":110,"depth":17,"authority":305,"freshness":98,"relevant":21,"comment":306},16,13,"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。",[308],{"name":300,"url":297},[26,27,310,28,311],"农业物联网","土壤墒情",{"code":63,"slug":127,"name":128,"path":129,"parent_code":36},[314,315],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":53,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":14,"version":40,"method":41,"evidence":317},{"candidates":318,"agricultural_evidence":46},[319,323,327,332,336,340],{"code":63,"score":17,"supported":46,"title":320,"abstract":321,"tags":322},[148],[146,147,148],[310],{"code":32,"score":45,"supported":46,"title":324,"abstract":325,"tags":326},[48],[50,48],[27],{"code":53,"score":13,"supported":46,"title":328,"abstract":329,"tags":331},[],[136,120,330],"evapotranspiration",[28],{"code":267,"score":151,"supported":46,"title":333,"abstract":334,"tags":335},[],[270,271],[311],{"code":58,"score":151,"supported":46,"title":337,"abstract":338,"tags":339},[],[263,264],[26],{"code":341,"score":98,"supported":46,"title":342,"abstract":343,"tags":346},"G2",[],[344,345],"小农户","smallholder",[],[348,349],"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",4,{"doi":9,"openalex_id":353,"authors":354,"venue":300,"cited_by_count":88,"oa_url":297,"card":361,"direction":219,"ingested_from":95},"W7217836036",[355,357,359],{"name":356,"orcid":9},"Christian Athanas Mwakyusa",{"name":358,"orcid":9},"Allen Deogratius Mwalekwa",{"name":360,"orcid":9},"Gift Geofrey Mushi",{"tldr":362,"method":363,"finding":364,"direction":219,"opportunity":365},"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":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":10,"source_url":370,"published_at":373,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":377,"tags":379,"primary_topic":382,"secondary_topics":383,"classification":385,"search_phrases":414,"slug":417,"view_count":351,"doi":418,"paper":419,"created_at":436},3131,"Water stress detection from plant electrophysiology: A machine learning framework for irrigation management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112434","Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The resulting time-series data were segmented into tumbling windows of 1 min, 5 min, 30 min, 1 h and 6 h, then passed through a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Across multiple input time horizons, we found that a 30-minute look-back horizon strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Out-of-sample testing on withheld plant individuals provides methodological validation, confirming that the framework detects healthy-to-stress transitions despite inter-individual electrophysiological variability.Overall, we develop and provide a decision-support tool for agricultural practitioners and researchers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.","植物胁迫的快速检测是植物表型分析、精准农业和自动化作物管理的关键。特别是，高效的灌溉管理需要早期识别水分胁迫，以优化资源利用并维持作物表现。直接生理传感有望在可见症状出现之前检测到胁迫响应。在本研究中，我们记录了温室种植的番茄植株在水分胁迫下的电生理信号，并开发了一个基于机器学习的在线胁迫检测框架。所得时间序列数据被分割为1分钟、5分钟、30分钟、1小时和6小时的滑动窗口，然后通过一个处理流程，包括统计特征提取与选择、自动化机器学习或深度学习，以及概率校准。在多个输入时间跨度中，我们发现30分钟的回溯窗口在快速决策与分类性能之间取得了最佳平衡。使用自动化机器学习，该框架达到了高达92%的分类准确率，优于深度学习方法。序列后向选择在保持性能的同时减少了特征集。对保留的植株个体进行样本外测试提供了方法学验证，确认该框架能够在个体间电生理变异性存在的情况下检测从健康到胁迫的转变。总体而言，我们为农业从业者和研究人员开发并提供了一种决策支持工具，并为生物反馈驱动的灌溉控制奠定了基础，以提高（半）自主作物生产系统中的资源效率。","2026-09-21T00:00:00Z",81,{"impact":17,"substance":110,"depth":17,"authority":19,"freshness":112,"relevant":21,"comment":376},"该研究提出基于植物电生理信号与机器学习的在线水分胁迫检测框架，30分钟窗口下准确率达92%，为精准灌溉与生物反馈控制提供决策支持，方法新颖、结论可靠，值得进入每日精选。",[378],{"name":10,"url":370},[26,27,28,380,381],"水分胁迫","植物电生理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[384],{"code":53,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":14,"version":40,"method":41,"evidence":386},{"candidates":387,"agricultural_evidence":46},[388,392,396,400,407],{"code":32,"score":17,"supported":46,"title":389,"abstract":390,"tags":391},[48],[250,50,48,252],[27],{"code":53,"score":45,"supported":46,"title":393,"abstract":394,"tags":395},[120],[136,120],[28],{"code":58,"score":151,"supported":46,"title":397,"abstract":398,"tags":399},[],[263,264],[26],{"code":401,"score":98,"supported":46,"title":402,"abstract":403,"tags":406},"B1",[],[404,405],"番茄","tomato",[],{"code":408,"score":98,"supported":46,"title":409,"abstract":410,"tags":413},"B7",[],[411,412],"温室","greenhouse",[],[415,416],"植物电生理 水分胁迫 机器学习","番茄 水分胁迫 灌溉管理","植物电生理水分胁迫机器学习-3131","10.1016\u002Fj.compag.2026.112434",{"doi":418,"openalex_id":420,"authors":421,"venue":10,"cited_by_count":88,"oa_url":370,"card":431,"direction":217,"ingested_from":95},"W7204541435",[422,425,428],{"name":423,"orcid":424},"Eduard Buss","https:\u002F\u002Forcid.org\u002F0000-0001-6993-5873",{"name":426,"orcid":427},"Till Aust","https:\u002F\u002Forcid.org\u002F0000-0003-2863-1341",{"name":429,"orcid":430},"Heiko Hamann","https:\u002F\u002Forcid.org\u002F0000-0002-2458-8289",{"tldr":432,"method":433,"finding":434,"direction":93,"opportunity":435},"利用番茄电生理信号与机器学习实现水分胁迫早期在线检测。","温室番茄电生理时序，滑窗分段、特征选择、自动机器学习与概率校准。","30分钟回溯窗口最优，自动机器学习分类准确率达92%，优于深度学习。","可探索多作物电生理泛化、田间部署及闭环灌溉控制，提升资源效率。","2026-09-22T23:30:02.024960Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":9,"source_name":443,"source_url":440,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":445,"score_detail":446,"sources":448,"tags":450,"primary_topic":451,"secondary_topics":454,"classification":460,"search_phrases":488,"slug":491,"view_count":71,"doi":492,"paper":493,"created_at":517},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":17,"substance":110,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":447},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[449],{"name":443,"url":440},[26,27,167,117,28],{"code":164,"slug":168,"name":167,"path":452,"parent_code":453},"\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[455,459],{"code":158,"slug":456,"name":457,"path":458,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":461},{"candidates":462,"agricultural_evidence":46},[463,469,474,478,484],{"code":164,"score":464,"supported":46,"title":465,"abstract":467,"tags":468},26,[466,168],"winter wheat",[167,466,168],[167],{"code":158,"score":17,"supported":46,"title":470,"abstract":471,"tags":473},[161],[117,161,472],"multispectral",[117],{"code":32,"score":45,"supported":46,"title":475,"abstract":476,"tags":477},[48],[50,48],[27],{"code":53,"score":13,"supported":46,"title":479,"abstract":480,"tags":483},[],[136,481,120,482],"水资源","water resource",[28],{"code":58,"score":151,"supported":46,"title":485,"abstract":486,"tags":487},[],[263,264],[26],[489,490],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":492,"openalex_id":494,"authors":495,"venue":443,"cited_by_count":88,"oa_url":440,"card":512,"direction":217,"ingested_from":95},"W7213246708",[496,499,501,503,506,509],{"name":497,"orcid":498},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":500,"orcid":9},"Qian Cheng",{"name":502,"orcid":9},"Fuyi Duan",{"name":504,"orcid":505},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":507,"orcid":508},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":510,"orcid":511},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":513,"method":514,"finding":515,"direction":217,"opportunity":516},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":519,"title":520,"url":521,"summary":522,"summary_zh":523,"content":9,"source_name":524,"source_url":521,"published_at":525,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":526,"score_detail":527,"sources":531,"tags":533,"primary_topic":534,"secondary_topics":535,"classification":540,"search_phrases":560,"slug":563,"view_count":21,"doi":564,"paper":565,"created_at":584},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",72,{"impact":305,"substance":528,"depth":529,"authority":305,"freshness":20,"relevant":21,"comment":530},21,17,"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[532],{"name":524,"url":521},[26,27,50,28,311],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[536,539],{"code":267,"slug":271,"name":537,"path":538,"parent_code":123},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":53,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":14,"version":40,"method":41,"evidence":541},{"candidates":542,"agricultural_evidence":46},[543,547,551,556],{"code":32,"score":304,"supported":46,"title":544,"abstract":545,"tags":546},[48],[50,48],[27,50],{"code":267,"score":45,"supported":46,"title":548,"abstract":549,"tags":550},[271],[270,271],[311],{"code":53,"score":13,"supported":46,"title":552,"abstract":553,"tags":555},[],[136,554,120,330],"蒸散发",[28],{"code":58,"score":112,"supported":46,"title":557,"abstract":558,"tags":559},[264],[],[26],[561,562],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":564,"openalex_id":566,"authors":567,"venue":524,"cited_by_count":88,"oa_url":9,"card":578,"direction":582,"ingested_from":95},"W7213181547",[568,571,574,576],{"name":569,"orcid":570},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":572,"orcid":573},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":575,"orcid":9},"Kinnoree R. Pasha",{"name":577,"orcid":9},"Dilruba Yeasmin",{"tldr":579,"method":580,"finding":581,"direction":582,"opportunity":583},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","农业人工智能与决策模型","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z"]