[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3160":3,"related-3160":111},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":110},3160,"Determination of Water Stress in Okra Plants Using Canopy Temperature Measurement by Infrared Thermometer","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84906","Water scarcity represents one of the most severe constraints on agricultural productivity in semi-arid and waterstressed cultivation zones. In traditional vegetable cultivation, farmers predominantly rely on visual symptoms of wilting to detect water deficit stress, which typically manifest only after substantial physiological damage, cellular dehydration, and irreversible yield loss have occurred. This study presents a non-destructive, precision agriculture framework for the early detection and classification of water stress in okra (Abelmoschus esculentus) using handheld infrared thermometry coupled with an ensemble Random Forest machine learning classifier. A primary field dataset comprising 100 observations was acquired under variable diurnal meteorological conditions in Jammu, India. The recorded and derived parameters encompassed canopy surface temperature (Tc), wet-bulb reference temperature (Twet), dry-bulb reference temperature (Tdry), ambient air temperature (Ta), relative humidity (RH), air saturation vapor pressure (SVPair), actual vapor pressure (AVP), leaf saturation vapor pressure (SVPleaf), leaf vapor pressure deficit (VPDleaf), canopy-air thermal differential (Tc – Ta), and the Crop Water Stress Index (CWSI). Using an empirical CWSI threshold of 0.30, samples were categorized into non-stressed (CWSI ≤ 0.30) and stressed (CWSI > 0.30) physiological states. The dataset was partitioned into an 80:20 training and testing split (80 training samples, 20 testing samples). The trained Random Forest classifier achieved an overall classification accuracy of 95.0% (19\u002F20 correct classifications) on the unseen test set. For the non-stressed class, the model demonstrated a precision of 1.00, recall of 0.92, and an F1-score of 0.96 (support = 12). For the water-stressed class, the model yielded a precision of 0.89, recall of 1.00, and an F1- score of 0.94 (support = 8), with zero false negatives (FN = 0), ensuring that no stressed crops were missed. Gini feature importance analysis revealed that dry-bulb reference temperature (Tdry, score = 0.187), leaf saturation vapor pressure (SVPleaf, score = 0.172), wet-bulb reference temperature (Twet, score = 0.160), leaf vapor pressure deficit (VPDleaf, score = 0.112), and Tc – Ta (score = 0.101) were the primary drivers governing classification. The findings confirm that coupling thermal radiometry with psychrometric feature engineering and ensemble learning provides a reliable, non-contact diagnostic mechanism for precision irrigation scheduling in smallholder horticulture.","水资源短缺是半干旱及水分胁迫耕作区农业生产力的最严重制约因素之一。在传统蔬菜种植中，农民主要依赖萎蔫的视觉症状来检测水分亏缺胁迫，而这类症状通常只有在发生大量生理损伤、细胞脱水及不可逆产量损失之后才会显现。本研究提出了一种非破坏性精准农业框架，利用手持式红外测温仪结合集成随机森林机器学习分类器，实现对秋葵（Abelmoschus esculentus）水分胁迫的早期检测与分类。在印度查谟地区多变的气象日变化条件下，采集了包含100个观测值的初始田间数据集。记录及衍生的参数包括冠层表面温度（Tc）、湿球参考温度（Twet）、干球参考温度（Tdry）、环境气温（Ta）、相对湿度（RH）、空气饱和水汽压（SVPair）、实际水汽压（AVP）、叶片饱和水汽压（SVPleaf）、叶片水汽压亏缺（VPDleaf）、冠层-空气温差（Tc – Ta）以及作物水分胁迫指数（CWSI）。采用经验性CWSI阈值0.30，将样本划分为非胁迫（CWSI ≤ 0.30）和胁迫（CWSI > 0.30）生理状态。数据集按80:20划分为训练集和测试集（80个训练样本，20个测试样本）。训练后的随机森林分类器在未见测试集上实现了95.0%的总体分类准确率（20个中正确分类19个）。对于非胁迫类别，模型精确率为1.00，召回率为0.92，F1分数为0.96（支持样本数=12）。对于水分胁迫类别，模型精确率为0.89，召回率为1.00，F1分数为0.94（支持样本数=8），假阴性为零（FN = 0），确保无胁迫作物被漏检。基尼特征重要性分析表明，干球参考温度（Tdry，得分=0.187）、叶片饱和水汽压（SVPleaf，得分=0.172）、湿球参考温度（Twet，得分=0.160）、叶片水汽压亏缺（VPDleaf，得分=0.112）以及Tc – Ta（得分=0.101）是主导分类的主要驱动因素。研究结果证实，将热辐射测量与湿度特征工程及集成学习相结合，可提供一种可靠的非接触式诊断机制",null,"International Journal for Research in Applied Science and Engineering Technology","2026-09-21T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,20,16,9,1,"印度查谟地区小样本田间研究，红外测温结合随机森林实现秋葵水分胁迫早期识别，方法可迁移至小农精准灌溉，但样本量仅100条、地域局限，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","机器学习","作物水分胁迫","精准灌溉","红外测温",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":81},[38,47,54,60,68,75],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":46},"H3",7,true,[],[44,45],"水资源","irrigation",[29],{"code":48,"score":40,"supported":41,"title":49,"abstract":50,"tags":53},"I1",[],[51,52],"精准农业","precision agriculture",[26],{"code":55,"score":40,"supported":41,"title":56,"abstract":57,"tags":59},"I2",[],[27,58],"machine learning",[27],{"code":61,"score":62,"supported":41,"title":63,"abstract":64,"tags":67},"B1",6,[],[65,66],"蔬菜","vegetable",[],{"code":69,"score":70,"supported":41,"title":71,"abstract":72,"tags":74},"G2",3,[],[73],"smallholder",[],{"code":76,"score":70,"supported":41,"title":77,"abstract":78,"tags":80},"H7",[],[79],"干旱",[],"多个产业证据相当，需要确认主归属",[83,84],"秋葵 冠层温度 水分胁迫","红外测温 作物水分胁迫指数","秋葵冠层温度水分胁迫-3160",4,"10.22214\u002Fijraset.2026.84906",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7213933715",[91,93,95,97,99],{"name":92,"orcid":9},"Muneeb Ajmer",{"name":94,"orcid":9},"Sayam Prajapati",{"name":96,"orcid":9},"Somil Narang",{"name":98,"orcid":9},"Rudraksh Sharma",{"name":100,"orcid":9},"Saksham Khajuria",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"用红外测温仪测秋葵冠层温度并结合随机森林，实现水分胁迫的早期无损分类。","手持红外测温获取冠层温度与气象参数，计算CWSI，用随机森林分类。","随机森林测试集准确率95%，无漏判胁迫样本，Tdry、SVPleaf等为关键特征。","农业遥感与作物表型","可扩展到多作物、多生育期及无人机热红外尺度，验证CWSI阈值与模型迁移性。","智慧农业 \u002F 农业物联网","openalex","2026-09-22T23:30:11.117810Z",{"total":62,"page":21,"page_size":62,"items":112},[113,237,298,369,432,502],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":136,"classification":147,"search_phrases":208,"slug":211,"view_count":101,"doi":212,"paper":213,"created_at":236},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":123,"substance":124,"depth":123,"authority":125,"freshness":20,"relevant":21,"comment":126},18,22,12,"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。",[128],{"name":119,"url":116},[26,130,131,28,29],"农业人工智能","遥感",{"code":39,"slug":45,"name":133,"path":134,"parent_code":135},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[137,142],{"code":55,"slug":138,"name":139,"path":140,"parent_code":141},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":143,"slug":144,"name":145,"path":146,"parent_code":141},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":148,"locked":14,"version":34,"method":35,"evidence":149},"classified",{"candidates":150,"agricultural_evidence":41},[151,157,162,169,175,181,188,195,202],{"code":39,"score":152,"supported":41,"title":153,"abstract":154,"tags":156},15,[45],[155,45],"灌溉",[29],{"code":55,"score":13,"supported":41,"title":158,"abstract":159,"tags":161},[],[27,58,160],"artificial intelligence",[130],{"code":143,"score":20,"supported":41,"title":163,"abstract":164,"tags":168},[],[165,166,167],"物联网","internet of things","IoT",[],{"code":48,"score":40,"supported":41,"title":170,"abstract":171,"tags":174},[],[172,173],"数字孪生","digital twin",[26],{"code":176,"score":40,"supported":41,"title":177,"abstract":178,"tags":180},"I3",[],[131,179],"remote sensing",[131],{"code":182,"score":62,"supported":41,"title":183,"abstract":184,"tags":187},"A2",[],[185,186],"小麦","wheat",[],{"code":189,"score":62,"supported":41,"title":190,"abstract":191,"tags":194},"A3",[],[192,193],"玉米","maize",[],{"code":196,"score":62,"supported":41,"title":197,"abstract":198,"tags":201},"A5",[],[199,200],"马铃薯","potato",[],{"code":203,"score":70,"supported":41,"title":204,"abstract":205,"tags":207},"B2",[],[206],"葡萄",[],[209,210],"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":212,"openalex_id":214,"authors":215,"venue":119,"cited_by_count":101,"oa_url":116,"card":231,"direction":108,"ingested_from":109},"W7223088921",[216,219,222,225,228],{"name":217,"orcid":218},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":220,"orcid":221},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":223,"orcid":224},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":226,"orcid":227},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":229,"orcid":230},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":232,"method":233,"finding":234,"direction":106,"opportunity":235},"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。","2026-10-22T11:20:24.526683Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":250,"tags":252,"primary_topic":255,"secondary_topics":256,"classification":257,"search_phrases":272,"slug":275,"view_count":276,"doi":277,"paper":278,"created_at":297},3837,"Comparative Scrutiny of Okra Lines for Water Stress by Plant Leaf Temperature Using Thermal Imaging","https:\u002F\u002Fdoi.org\u002F10.1080\u002F00103624.2026.2736711","Canopy temperature is widely utilized as a proxy for evaluating plant water stress. Recent technological innovations have advanced agricultural practices, transitioning from leaf-mounted sensors to short-range remote sensing techniques, such as infrared thermometry and thermal imaging. Although thermal imaging is well-documented for assessing crop water stress, comparative research on lines of intra-species under water deficit is scarce. This study aimed to evaluate the feasibility of usingtermal imaging to compare water deficit responses among different okra lines based on plant leaf temperature. To achieve this, leaf temperatures of 10 different okra lines were assessed across four different irrigation regimes: full irrigation, and mild, moderate and severe water deficits. The results show that pre-irrigation thermal image data reflect water deficits and line-specific differences more accurately than post-irrigation measurements. Across almost all lines, the highest leaf temperatures occurred under severe water deficit, while the lowest were recorded under full irrigation. Notably, lines, MGL-4-SH-2, MGL-5-SH-2, MGL-7-SH-2 and MGL-8-SH-2 demonstrated greater resistance to water deficit, consistently exhibiting the lowest leaf temperatures. In conclusion, thermal cameras effectively measure water stress in okra and successfully differentiate line-specific responses to deficit water conditions.","冠层温度被广泛用作评估植物水分胁迫的替代指标。近年来的技术创新推动了农业实践的进步，从叶片安装式传感器发展到短距离遥感技术，如红外测温法和热成像。尽管热成像在评估作物水分胁迫方面已有充分文献记载，但针对水分亏缺条件下种内品系的比较研究仍然匮乏。本研究旨在评估利用热成像技术基于植物叶片温度比较不同秋葵品系对水分亏缺响应的可行性。为此，研究评估了10个不同秋葵品系在四种不同灌溉制度下的叶片温度：充分灌溉以及轻度、中度和重度水分亏缺。结果表明，灌溉前的热成像数据比灌溉后测量数据更能准确反映水分亏缺和品系特异性差异。在几乎所有品系中，最高叶片温度出现在重度水分亏缺条件下，而最低叶片温度记录于充分灌溉条件下。值得注意的是，品系MGL-4-SH-2、MGL-5-SH-2、MGL-7-SH-2和MGL-8-SH-2表现出更强的水分亏缺抗性，始终呈现最低的叶片温度。总之，热像仪能够有效测量秋葵的水分胁迫，并成功区分不同品系对水分亏缺条件的响应。","Communications in Soil Science and Plant Analysis","2026-09-29T00:00:00Z",70,{"impact":125,"substance":18,"depth":247,"authority":248,"freshness":17,"relevant":21,"comment":249},17,13,"热成像遥感评估秋葵品系水分胁迫，方法新颖、数据扎实，对精准灌溉与抗旱育种有参考价值，但属细分作物研究，影响范围有限。",[251],{"name":243,"url":240},[26,28,29,253,254],"热成像遥感","秋葵育种",{"code":39,"slug":45,"name":133,"path":134,"parent_code":135},[],{"status":148,"locked":14,"version":34,"method":35,"evidence":258},{"candidates":259,"agricultural_evidence":41},[260,264,268],{"code":39,"score":40,"supported":41,"title":261,"abstract":262,"tags":263},[],[155,45],[29],{"code":176,"score":62,"supported":41,"title":265,"abstract":266,"tags":267},[],[131,179],[],{"code":48,"score":21,"supported":14,"title":269,"abstract":270,"tags":271},[],[],[26],[273,274],"秋葵 水分胁迫 热成像","秋葵品系 叶片温度","秋葵水分胁迫热成像-3837",2,"10.1080\u002F00103624.2026.2736711",{"doi":277,"openalex_id":279,"authors":280,"venue":243,"cited_by_count":101,"oa_url":9,"card":292,"direction":106,"ingested_from":109},"W7214721913",[281,283,286,289],{"name":282,"orcid":9},"Esra Aytaç",{"name":284,"orcid":285},"Nefise Yasemin Tezcan","https:\u002F\u002Forcid.org\u002F0000-0002-3384-0108",{"name":287,"orcid":288},"Ahmet Kurunç","https:\u002F\u002Forcid.org\u002F0000-0002-1983-8415",{"name":290,"orcid":291},"Faik Kantar","https:\u002F\u002Forcid.org\u002F0000-0003-3102-1181",{"tldr":293,"method":294,"finding":295,"direction":106,"opportunity":296},"用热成像测叶片温度，比较10个秋葵品系在四种水分亏缺下的响应差异。","热成像获取灌溉前后叶温，设全灌及轻中重度亏缺四处理。","灌溉前热图更能反映水分亏缺和品系差异，4个品系耐旱性最强。","可拓展至多作物、多生育期的高通量耐旱品系筛选与自动灌溉决策。","2026-09-30T23:30:27.699916Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":310,"tags":312,"primary_topic":314,"secondary_topics":315,"classification":322,"search_phrases":344,"slug":347,"view_count":21,"doi":348,"paper":349,"created_at":368},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":248,"substance":308,"depth":247,"authority":248,"freshness":17,"relevant":21,"comment":309},21,"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。",[311],{"name":304,"url":301},[26,130,27,29,313],"土壤墒情",{"code":55,"slug":138,"name":139,"path":140,"parent_code":141},[316,321],{"code":317,"slug":318,"name":319,"path":320,"parent_code":135},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":39,"slug":45,"name":133,"path":134,"parent_code":135},{"status":148,"locked":14,"version":34,"method":35,"evidence":323},{"candidates":324,"agricultural_evidence":41},[325,329,334,340],{"code":55,"score":19,"supported":41,"title":326,"abstract":327,"tags":328},[58],[27,58],[130,27],{"code":317,"score":152,"supported":41,"title":330,"abstract":331,"tags":333},[318],[332,318],"土壤",[313],{"code":39,"score":13,"supported":41,"title":335,"abstract":336,"tags":339},[],[155,337,45,338],"蒸散发","evapotranspiration",[29],{"code":48,"score":20,"supported":41,"title":341,"abstract":342,"tags":343},[52],[],[26],[345,346],"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":348,"openalex_id":350,"authors":351,"venue":304,"cited_by_count":101,"oa_url":9,"card":362,"direction":366,"ingested_from":109},"W7213181547",[352,355,358,360],{"name":353,"orcid":354},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":356,"orcid":357},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":359,"orcid":9},"Kinnoree R. Pasha",{"name":361,"orcid":9},"Dilruba Yeasmin",{"tldr":363,"method":364,"finding":365,"direction":366,"opportunity":367},"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":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":377,"sources":379,"tags":381,"primary_topic":9,"secondary_topics":382,"classification":383,"search_phrases":411,"slug":414,"view_count":62,"doi":415,"paper":416,"created_at":431},2437,"Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52151\u002Fjae2026634.2043","Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-consuming, labour-intensive and limited in spatial coverage, restricting their applicability for regional-scale monitoring. Remote sensing integrated with machine learning provides a promising alternative for generating spatially continuous and timely soil moisture estimates. This study aimed to predict surface soil moisture in the Ranipool-Rumtek administrative region of Sikkim, India, using multimodal remote sensing data and machine-learning techniques. Soil moisture was measured at 85 locations using the gravimetric method and these same locations were used as ground truthing sites. Multi-temporal imagery from Landsat-8 and Sentinel-2 was processed to derive vegetation and moisture-related indices, i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Shortwave-infrared Difference Soil Moisture Index (NSDSI3), Land Surface Temperature (LST), Moisture Stress Index (MSI), and Vegetation Supply Water Index (VSWI). These indices were used as predictor variables to develop artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) models. The results revealed that the ANN model developed using Sentinel-2-derived indices exhibited the highest predictive accuracy, achieving values of coefficient of determination (R2) as 0.82, Root Mean Square Error (RMSE) as 6% and Mean Absolute Error (MAE) of 4.6%. The performance of the Sentinel-2-derived ANN model was found to be better than that of the Landsat-8-based ANN model as well as the SVM and MLR models developed using Sentinel-2 data. The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments. The proposed approach provides a robust framework to support precision agriculture, irrigation scheduling, hydrological modelling, and drought and flood monitoring.","土壤水分是影响农业生产力、水文过程和土地管理的重要变量，尤其是在印度东北丘陵邦（NEH）等高降雨地区。尽管传统土壤水分测量技术能够提供可靠的观测数据，但其耗时、费力且空间覆盖有限，限制了其在区域尺度监测中的适用性。遥感与机器学习相结合，为生成空间连续且及时的土壤水分估算提供了一种有前景的替代方案。本研究旨在利用多模态遥感数据和机器学习技术，预测印度锡金邦拉尼普尔-鲁姆特克行政区的表层土壤水分。采用重量法在85个位置测量了土壤水分，并将这些位置作为地面验证点。对Landsat-8和Sentinel-2的多时相影像进行处理，以提取植被和水分相关指数，即归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、归一化差异水分指数（NDMI）、归一化短波红外差异土壤水分指数（NSDSI3）、地表温度（LST）、水分胁迫指数（MSI）和植被供水指数（VSWI）。这些指数被用作预测变量，以构建人工神经网络（ANN）、支持向量机（SVM）和多元线性回归（MLR）模型。结果表明，使用Sentinel-2衍生指数构建的ANN模型预测精度最高，决定系数（R²）达到0.82，均方根误差（RMSE）为6%，平均绝对误差（MAE）为4.6%。Sentinel-2衍生的ANN模型性能优于基于Landsat-8的ANN模型以及使用Sentinel-2数据构建的SVM和MLR模型。较强的预测性能表明，将高分辨率Sentinel-2影像数据与ANN相结合，能够在地形起伏大、数据稀疏的环境中实现准确、可扩展且高效的土壤水分估算。所提出的方法为支持精准农业、灌溉调度、水文建模以及旱涝监测提供了一个稳健的框架。","Journal of Agricultural Engineering (India)","2026-09-11T00:00:00Z",{"impact":152,"substance":18,"depth":247,"authority":125,"freshness":62,"relevant":21,"comment":378},"基于多模态遥感与人工神经网络的土壤墒情预测研究，方法新颖、精度可靠，对高降雨山区精准农业与灌溉调度有参考价值，但属区域性案例，影响范围有限。",[380],{"name":375,"url":372},[26,27,131,29,313],[],{"status":33,"locked":14,"version":34,"method":35,"evidence":384},{"candidates":385,"agricultural_evidence":41,"reason":81},[386,390,394,398,402,406],{"code":317,"score":152,"supported":41,"title":387,"abstract":388,"tags":389},[318],[332,318],[313],{"code":55,"score":152,"supported":41,"title":391,"abstract":392,"tags":393},[58],[27,58],[27],{"code":176,"score":152,"supported":41,"title":395,"abstract":396,"tags":397},[179],[131,179],[131],{"code":39,"score":40,"supported":41,"title":399,"abstract":400,"tags":401},[],[155,45],[29],{"code":48,"score":40,"supported":41,"title":403,"abstract":404,"tags":405},[],[51,52],[26],{"code":76,"score":70,"supported":41,"title":407,"abstract":408,"tags":410},[],[409],"drought",[],[412,413],"土壤墒情 智慧农业 机器学习 精准灌溉","土壤墒情 智慧农业","土壤墒情智慧农业机器学习精准灌溉-2437","10.52151\u002Fjae2026634.2043",{"doi":415,"openalex_id":417,"authors":418,"venue":375,"cited_by_count":101,"oa_url":9,"card":426,"direction":106,"ingested_from":109},"W7212454193",[419,421,424],{"name":420,"orcid":9},"Pranjal Dubey",{"name":422,"orcid":423},"G. T. Patle","https:\u002F\u002Forcid.org\u002F0000-0002-9175-8567",{"name":425,"orcid":9},"Vinay Kumar Gautam",{"tldr":427,"method":428,"finding":429,"direction":106,"opportunity":430},"用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。","Landsat-8与Sentinel-2植被\u002F水分指数，ANN、SVM、MLR建","Sentinel-2指数驱动的ANN精度最高，R²=0.82、RMSE=6%，优于Landsat-8","可探索多源时序遥感与深度学习融合，提升高降雨山区土壤水分时空连续估算与业务化监测能力。","2026-09-14T23:30:27.771449Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":440,"sources":443,"tags":445,"primary_topic":447,"secondary_topics":450,"classification":452,"search_phrases":480,"slug":483,"view_count":70,"doi":484,"paper":485,"created_at":501},1905,"A novel surface reflectance calibration approach to improve the estimation of maize actual evapotranspiration in a semi-arid USA region","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727171","This study aimed to develop a novel surface reflectance (SR) data calibration approach to improve maize actual crop evapotranspiration (ETa, mm h−1) predictions in agricultural settings. The remote sensing (RS) platforms used in the study included Landsat-8 (30 m spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld radiometer (1 m), and a small uncrewed aerial system or sUAS (0.03 m). A one-source surface energy balance (OSEB) was applied to estimate ETa and assess any improvements when using original and calibrated SR data. Five machine-learning (ML) algorithms were evaluated as part of the calibration process. The ML approaches were linear regression, regression tree, random forest, support vector machine, and Gaussian process regression. The SR calibration approach incorporated a novel dual-source pixel decomposition model that accounts for the contributions of soil and vegetation light reflectance captured by a given SR band. Two maize research sites, one in Greeley and another in Fort Collins, Colorado, U.S.A. provided the grounds for data collection over a four-year period. Results indicated that the best ML model, for a given RS sensor, depended on the SR contributions from plants and bare soil. Among the evaluated algorithms, the Regression Tree and Gaussian Process Regression provided the most accurate pixel decomposition and reflectance adjustments, consistently demonstrating the highest statistical agreement (R2 = 0.70–0.94) and lowest residual errors (RMSE = 0.004–0.022). Improvements in maize hourly ETa had a 34% error reduction in maize ETa estimation for data from the handheld radiometer and sUAS, compared to a 12% error reduction for the spaceborne sensors, on average.","本研究旨在开发一种新型地表反射率（SR）数据校准方法，以提高农业环境中玉米实际作物蒸散量（ETa, mm h⁻¹）的预测精度。研究中使用的遥感（RS）平台包括Landsat-8（空间分辨率30米）、Sentinel-2（10米）、Planet CubeSat（3米）、手持式辐射计（1米）以及小型无人机系统（sUAS，0.03米）。采用单源地表能量平衡（OSEB）模型估算ETa，并评估使用原始与校准SR数据时的改进效果。校准过程中评估了五种机器学习（ML）算法，包括线性回归、回归树、随机森林、支持向量机和高斯过程回归。SR校准方法引入了一种新型双源像元分解模型，该模型考虑了特定SR波段所捕获的土壤与植被光反射的贡献。研究选取了美国科罗拉多州格里利和柯林斯堡的两个玉米研究站点，在四年期间收集数据。结果表明，对于给定的遥感传感器，最佳ML模型取决于植被和裸土的SR贡献。在所评估的算法中，回归树和高斯过程回归提供了最精确的像元分解和反射率调整，始终表现出最高的一致性统计指标（R² = 0.70–0.94）和最低的残差误差（RMSE = 0.004–0.022）。在玉米小时ETa估算改进方面，手持式辐射计和sUAS数据的ETa估算误差平均降低了34%，而星载传感器的误差平均降低了12%。","International Journal of Remote Sensing","2026-09-06T00:00:00Z",{"impact":152,"substance":124,"depth":123,"authority":441,"freshness":70,"relevant":21,"comment":442},14,"研究提出地表反射率校准新方法，结合多源遥感与机器学习提升玉米蒸散估算精度，对农业水资源管理有参考价值。",[444],{"name":438,"url":435},[26,27,131,446,29],"作物模型",{"code":189,"slug":193,"name":192,"path":448,"parent_code":449},"\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[451],{"code":39,"slug":45,"name":133,"path":134,"parent_code":135},{"status":148,"locked":14,"version":34,"method":35,"evidence":453},{"candidates":454,"agricultural_evidence":41},[455,459,463,467,471,476],{"code":189,"score":441,"supported":41,"title":456,"abstract":457,"tags":458},[193],[192,193],[],{"code":39,"score":125,"supported":41,"title":460,"abstract":461,"tags":462},[338],[338],[29],{"code":176,"score":40,"supported":41,"title":464,"abstract":465,"tags":466},[],[131,179],[131],{"code":317,"score":62,"supported":41,"title":468,"abstract":469,"tags":470},[],[332,318],[],{"code":55,"score":472,"supported":41,"title":473,"abstract":474,"tags":475},5,[],[27],[446,27],{"code":48,"score":21,"supported":14,"title":477,"abstract":478,"tags":479},[],[],[26],[481,482],"作物模型 智慧农业 机器学习 精准灌溉","作物模型 智慧农业","作物模型智慧农业机器学习精准灌溉-1905","10.1080\u002F01431161.2026.2727171",{"doi":484,"openalex_id":486,"authors":487,"venue":438,"cited_by_count":101,"oa_url":9,"card":496,"direction":106,"ingested_from":109},"W7210284621",[488,491,494],{"name":489,"orcid":490},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":492,"orcid":493},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":495,"orcid":9},"Huihui Zhang",{"tldr":497,"method":498,"finding":499,"direction":106,"opportunity":500},"提出地表反射率校准方法，结合机器学习与双源像素分解，提高半干旱区玉米蒸散发估算精度。","多源遥感数据，OSEB模型，五种机器学习算法，双源像素分解模型。","回归树和高斯过程回归校准效果最佳，误差降低12%-34%，高分辨率传感器提升更显著。","可探索将校准方法应用于其他作物或区域，或结合深度学习提升低分辨率卫星数据的校准精度。","2026-09-08T23:30:20.154043Z",{"id":503,"title":504,"url":505,"summary":506,"summary_zh":9,"content":9,"source_name":507,"source_url":505,"published_at":508,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":509,"sources":511,"tags":513,"primary_topic":514,"secondary_topics":518,"classification":519,"search_phrases":536,"slug":539,"view_count":62,"doi":540,"paper":541,"created_at":552},1517,"Estimation of Agricultural Vegetation Water Stress Using Feature-Optimized Remote Sensing and Ensemble Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102536","Estimation of Agricultural Vegetation Water Stress Using Feature-Optimized Remote Sensing and Ensemble Machine Learning。Smart Agricultural Technology","Smart Agricultural Technology","2026-09-01T00:00:00Z",{"impact":152,"substance":18,"depth":123,"authority":125,"freshness":40,"relevant":21,"comment":510},"论文提出特征优化遥感与集成机器学习估算农业植被水分胁迫，方法新颖，数据详实，对精准灌溉有参考价值。",[512],{"name":507,"url":505},[26,27,131,28],{"code":141,"slug":515,"name":516,"path":517,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":520,"locked":14,"version":34,"method":35,"evidence":521},"partial",{"candidates":522,"agricultural_evidence":41,"reason":535},[523,527,531],{"code":55,"score":125,"supported":41,"title":524,"abstract":525,"tags":526},[58],[58],[27],{"code":176,"score":125,"supported":41,"title":528,"abstract":529,"tags":530},[179],[179],[131],{"code":48,"score":21,"supported":14,"title":532,"abstract":533,"tags":534},[],[],[26],"一级领域明确，二级方向需要细分",[537,538],"作物水分胁迫 智慧农业 机器学习 遥感","作物水分胁迫 智慧农业","作物水分胁迫智慧农业机器学习遥感-1517","10.1016\u002Fj.atech.2026.102536",{"doi":540,"openalex_id":542,"authors":543,"venue":507,"cited_by_count":101,"oa_url":505,"card":9,"direction":106,"ingested_from":109},"W7206189125",[544,547,549],{"name":545,"orcid":546},"Ahmed Elbeltagi","https:\u002F\u002Forcid.org\u002F0000-0002-5506-9502",{"name":548,"orcid":9},"Aman Srivastava",{"name":550,"orcid":551},"Abdullah A. Alsumaiei","https:\u002F\u002Forcid.org\u002F0000-0002-9148-3954","2026-09-03T23:30:04.462065Z"]