[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3131":3,"related-3131":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":115},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。",null,"Computers and Electronics in Agriculture","2026-09-21T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准灌溉","水分胁迫","植物电生理",{"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",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,59,65,73,81],{"code":32,"score":17,"supported":51,"title":52,"abstract":54,"tags":58},true,[53],"machine learning",[55,56,53,57],"深度学习","机器学习","deep learning",[27],{"code":39,"score":60,"supported":51,"title":61,"abstract":62,"tags":64},15,[40],[63,40],"灌溉",[28],{"code":66,"score":67,"supported":51,"title":68,"abstract":69,"tags":72},"I1",7,[],[70,71],"精准农业","precision agriculture",[26],{"code":74,"score":75,"supported":51,"title":76,"abstract":77,"tags":80},"B1",6,[],[78,79],"番茄","tomato",[],{"code":82,"score":75,"supported":51,"title":83,"abstract":84,"tags":87},"B7",[],[85,86],"温室","greenhouse",[],[89,90],"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",4,"10.1016\u002Fj.compag.2026.112434",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":113,"ingested_from":114},"W7204541435",[97,100,103],{"name":98,"orcid":99},"Eduard Buss","https:\u002F\u002Forcid.org\u002F0000-0001-6993-5873",{"name":101,"orcid":102},"Till Aust","https:\u002F\u002Forcid.org\u002F0000-0003-2863-1341",{"name":104,"orcid":105},"Heiko Hamann","https:\u002F\u002Forcid.org\u002F0000-0002-2458-8289",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"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。","其他","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。","农业遥感与作物表型","openalex","2026-09-22T23:30:02.024960Z",{"total":75,"page":21,"page_size":75,"items":117},[118,232,307,375,472,529],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":136,"classification":143,"search_phrases":202,"slug":205,"view_count":106,"doi":206,"paper":207,"created_at":231},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":18,"depth":17,"authority":128,"freshness":20,"relevant":21,"comment":129},12,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[131],{"name":124,"url":121},[26,27,133,134,28],"遥感","作物水分胁迫",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[137,138],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":139,"slug":140,"name":141,"path":142,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":144},{"candidates":145,"agricultural_evidence":51},[146,150,155,162,168,174,181,188,195],{"code":39,"score":60,"supported":51,"title":147,"abstract":148,"tags":149},[40],[63,40],[28],{"code":32,"score":13,"supported":51,"title":151,"abstract":152,"tags":154},[],[56,53,153],"artificial intelligence",[27],{"code":139,"score":20,"supported":51,"title":156,"abstract":157,"tags":161},[],[158,159,160],"物联网","internet of things","IoT",[],{"code":66,"score":67,"supported":51,"title":163,"abstract":164,"tags":167},[],[165,166],"数字孪生","digital twin",[26],{"code":169,"score":67,"supported":51,"title":170,"abstract":171,"tags":173},"I3",[],[133,172],"remote sensing",[133],{"code":175,"score":75,"supported":51,"title":176,"abstract":177,"tags":180},"A2",[],[178,179],"小麦","wheat",[],{"code":182,"score":75,"supported":51,"title":183,"abstract":184,"tags":187},"A3",[],[185,186],"玉米","maize",[],{"code":189,"score":75,"supported":51,"title":190,"abstract":191,"tags":194},"A5",[],[192,193],"马铃薯","potato",[],{"code":196,"score":197,"supported":51,"title":198,"abstract":199,"tags":201},"B2",3,[],[200],"葡萄",[],[203,204],"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":206,"openalex_id":208,"authors":209,"venue":124,"cited_by_count":106,"oa_url":121,"card":225,"direction":230,"ingested_from":114},"W7223088921",[210,213,216,219,222],{"name":211,"orcid":212},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":214,"orcid":215},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":217,"orcid":218},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":220,"orcid":221},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":223,"orcid":224},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":226,"method":227,"finding":228,"direction":113,"opportunity":229},"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。","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":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":245,"tags":247,"primary_topic":250,"secondary_topics":251,"classification":254,"search_phrases":288,"slug":291,"view_count":92,"doi":9,"paper":292,"created_at":306},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":242,"substance":18,"depth":17,"authority":243,"freshness":75,"relevant":21,"comment":244},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。",[246],{"name":238,"url":235},[26,27,248,28,249],"农业物联网","土壤墒情",{"code":139,"slug":140,"name":141,"path":142,"parent_code":36},[252,253],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":255},{"candidates":256,"agricultural_evidence":51},[257,261,265,270,277,281],{"code":139,"score":17,"supported":51,"title":258,"abstract":259,"tags":260},[160],[158,159,160],[248],{"code":32,"score":60,"supported":51,"title":262,"abstract":263,"tags":264},[53],[56,53],[27],{"code":39,"score":13,"supported":51,"title":266,"abstract":267,"tags":269},[],[63,40,268],"evapotranspiration",[28],{"code":271,"score":67,"supported":51,"title":272,"abstract":273,"tags":276},"H2",[],[274,275],"土壤","soil",[249],{"code":66,"score":67,"supported":51,"title":278,"abstract":279,"tags":280},[],[70,71],[26],{"code":282,"score":75,"supported":51,"title":283,"abstract":284,"tags":287},"G2",[],[285,286],"小农户","smallholder",[],[289,290],"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":293,"authors":294,"venue":238,"cited_by_count":106,"oa_url":235,"card":301,"direction":230,"ingested_from":114},"W7217836036",[295,297,299],{"name":296,"orcid":9},"Christian Athanas Mwakyusa",{"name":298,"orcid":9},"Allen Deogratius Mwalekwa",{"name":300,"orcid":9},"Gift Geofrey Mushi",{"tldr":302,"method":303,"finding":304,"direction":230,"opportunity":305},"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":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":321,"tags":323,"primary_topic":324,"secondary_topics":325,"classification":330,"search_phrases":350,"slug":353,"view_count":21,"doi":354,"paper":355,"created_at":374},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":243,"substance":317,"depth":318,"authority":243,"freshness":319,"relevant":21,"comment":320},21,17,8,"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。",[322],{"name":313,"url":310},[26,27,56,28,249],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[326,329],{"code":271,"slug":275,"name":327,"path":328,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":331},{"candidates":332,"agricultural_evidence":51},[333,337,341,346],{"code":32,"score":242,"supported":51,"title":334,"abstract":335,"tags":336},[53],[56,53],[27,56],{"code":271,"score":60,"supported":51,"title":338,"abstract":339,"tags":340},[275],[274,275],[249],{"code":39,"score":13,"supported":51,"title":342,"abstract":343,"tags":345},[],[63,344,40,268],"蒸散发",[28],{"code":66,"score":20,"supported":51,"title":347,"abstract":348,"tags":349},[71],[],[26],[351,352],"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":354,"openalex_id":356,"authors":357,"venue":313,"cited_by_count":106,"oa_url":9,"card":368,"direction":372,"ingested_from":114},"W7213181547",[358,361,364,366],{"name":359,"orcid":360},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":362,"orcid":363},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":365,"orcid":9},"Kinnoree R. Pasha",{"name":367,"orcid":9},"Dilruba Yeasmin",{"tldr":369,"method":370,"finding":371,"direction":372,"opportunity":373},"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",{"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":13,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":390,"secondary_topics":391,"classification":397,"search_phrases":436,"slug":439,"view_count":197,"doi":440,"paper":441,"created_at":471},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":17,"substance":18,"depth":17,"authority":128,"freshness":197,"relevant":21,"comment":385},"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[387],{"name":381,"url":378},[26,27,133,28,389],"牛油果",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[392,393],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":169,"slug":394,"name":395,"path":396,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":398},{"candidates":399,"agricultural_evidence":51},[400,405,409,415,421,425,432],{"code":39,"score":17,"supported":51,"title":401,"abstract":402,"tags":404},[40],[63,403,40],"水资源",[28],{"code":32,"score":128,"supported":51,"title":406,"abstract":407,"tags":408},[53],[153],[27],{"code":169,"score":13,"supported":51,"title":410,"abstract":411,"tags":414},[],[133,412,172,413],"无人机多光谱","multispectral",[133],{"code":196,"score":67,"supported":51,"title":416,"abstract":417,"tags":420},[],[418,419],"果树","orchard",[389],{"code":271,"score":75,"supported":51,"title":422,"abstract":423,"tags":424},[],[274,275],[],{"code":426,"score":75,"supported":51,"title":427,"abstract":428,"tags":431},"H7",[],[429,430],"干旱","drought",[],{"code":66,"score":21,"supported":14,"title":433,"abstract":434,"tags":435},[],[],[26],[437,438],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":440,"openalex_id":442,"authors":443,"venue":381,"cited_by_count":106,"oa_url":465,"card":466,"direction":113,"ingested_from":114},"W7211887459",[444,447,450,453,456,459,462],{"name":445,"orcid":446},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":448,"orcid":449},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":451,"orcid":452},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":454,"orcid":455},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":457,"orcid":458},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":460,"orcid":461},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":463,"orcid":464},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":467,"method":468,"finding":469,"direction":230,"opportunity":470},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":479,"score_detail":480,"sources":482,"tags":484,"primary_topic":488,"secondary_topics":489,"classification":491,"search_phrases":511,"slug":514,"view_count":106,"doi":515,"paper":516,"created_at":528},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)",69,{"impact":319,"substance":18,"depth":17,"authority":128,"freshness":20,"relevant":21,"comment":481},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[483],{"name":478,"url":475},[26,27,485,486,487],"灌溉决策","安全保证","试验设计",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[490],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":492},{"candidates":493,"agricultural_evidence":51},[494,498,503,507],{"code":39,"score":60,"supported":51,"title":495,"abstract":496,"tags":497},[40],[63,40],[485],{"code":32,"score":128,"supported":51,"title":499,"abstract":500,"tags":502},[153],[501],"decision support",[27],{"code":271,"score":75,"supported":51,"title":504,"abstract":505,"tags":506},[],[274,275],[],{"code":66,"score":21,"supported":14,"title":508,"abstract":509,"tags":510},[],[],[26],[512,513],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":515,"openalex_id":517,"authors":518,"venue":478,"cited_by_count":106,"oa_url":475,"card":523,"direction":372,"ingested_from":114},"W7223131205",[519,521],{"name":520,"orcid":9},"Nassima Benkhelifa",{"name":522,"orcid":9},"Yacine Aït Ahmed",{"tldr":524,"method":525,"finding":526,"direction":372,"opportunity":527},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":530,"title":531,"url":532,"summary":533,"summary_zh":534,"content":9,"source_name":535,"source_url":532,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":536,"score_detail":537,"sources":539,"tags":541,"primary_topic":543,"secondary_topics":544,"classification":547,"search_phrases":592,"slug":595,"view_count":106,"doi":596,"paper":597,"created_at":615},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":17,"substance":18,"depth":17,"authority":243,"freshness":20,"relevant":21,"comment":538},"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。",[540],{"name":535,"url":532},[26,27,158,70,542],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[545,546],{"code":139,"slug":140,"name":141,"path":142,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":548},{"candidates":549,"agricultural_evidence":51},[550,556,560,566,570,576,580,586],{"code":32,"score":17,"supported":51,"title":551,"abstract":552,"tags":555},[153],[55,56,553,53,57,554],"计算机视觉","computer vision",[27],{"code":66,"score":242,"supported":51,"title":557,"abstract":558,"tags":559},[71],[70,71],[26,70],{"code":139,"score":13,"supported":51,"title":561,"abstract":562,"tags":565},[],[158,563,160,564],"边缘计算","edge computing",[158],{"code":39,"score":20,"supported":51,"title":567,"abstract":568,"tags":569},[],[63,403,40],[],{"code":426,"score":67,"supported":51,"title":571,"abstract":572,"tags":575},[],[573,574],"气候变化","climate change",[542],{"code":271,"score":75,"supported":51,"title":577,"abstract":578,"tags":579},[],[274,275],[],{"code":581,"score":197,"supported":51,"title":582,"abstract":583,"tags":585},"A7",[],[584],"间作",[],{"code":587,"score":197,"supported":51,"title":588,"abstract":589,"tags":591},"F3",[],[590],"养分管理",[],[593,594],"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":596,"openalex_id":598,"authors":599,"venue":535,"cited_by_count":106,"oa_url":532,"card":610,"direction":230,"ingested_from":114},"W7223121456",[600,603,605,607],{"name":601,"orcid":602},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":604,"orcid":9},"Oluwadare OYEBODE",{"name":606,"orcid":9},"Iretiayo Adelaiye",{"name":608,"orcid":609},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":611,"method":612,"finding":613,"direction":372,"opportunity":614},"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。","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"]