[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3509":3,"related-3509":91},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":90},3509,"Integrating multi-criteria decision-making and remote sensing for wildfire susceptibility assessment of the Lower Ziz Valley oases in Morocco","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00735-8","Oasis ecosystems in North Africa are ecologically and economically vital landscapes. Yet they are increasingly threatened by wildfires driven by intensifying climate change and anthropogenic pressures. Despite this urgency, spatially explicit wildfire susceptibility assessments for arid oasis environments remain scarce. This study develops and validates a Fire Susceptibility Index (FSI) for the Lower Ziz Valley oases in southeastern Morocco using an integrated Multi-Criteria Decision-Making (MCDM) and remote sensing framework. Six conditioning factors were selected: Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), proximity to roads and settlements, and land cover classified via a U-Net deep learning model (overall accuracy: 95%). Factor weights were determined through the Analytic Hierarchy Process (AHP; CR = 0.09) and aggregated using Weighted Linear Combination (WLC). Land cover (w = 0.30) and NDVI (w = 0.25) emerged as dominant controls, with anthropogenic proximity factors jointly accounting for 31% of the index weight. FSI values ranged from 0.30 to 0.91 (mean = 0.67), with 42.7% of the oasis area (~ 1,707 ha) classified as high susceptibility (FSI ≥ 0.7). Validation against the August 2021 fire event (318 ha) demonstrated strong predictive performance: 96.5% of burned pixels fell within high-risk zones, and mean FSI was significantly higher for burned (0.72) than unburned (0.67) pixels ( p \u003C 0.001), with an AUC of 0.703. Corroborating validation against 60 historical fire records (2009–2024) showed that 98% of events occurred in high or extreme susceptibility zones. The resulting FSI map provides a robust spatial basis for prioritizing fuel management interventions and fire prevention strategies. Its integration into a dynamic geodatabase could further support the development of an early warning system (EWS) for operational wildfire prediction in these irreplaceable oasis landscapes.","北非的绿洲生态系统是具有重要生态和经济价值的景观。然而，日益加剧的气候变化和人为压力所驱动的野火正对其构成越来越大的威胁。尽管形势紧迫，针对干旱绿洲环境的空间显式野火易发性评估仍然匮乏。本研究采用多准则决策（MCDM）与遥感相结合的综合框架，为摩洛哥东南部下济兹河谷绿洲开发并验证了火灾易发性指数（FSI）。选取了六个条件因子：地表温度（LST）、归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）、道路与居民点邻近度，以及通过U-Net深度学习模型分类的土地覆盖（总体精度：95%）。因子权重通过层次分析法（AHP；CR = 0.09）确定，并采用加权线性组合（WLC）进行聚合。土地覆盖（w = 0.30）和NDVI（w = 0.25）成为主导控制因子，人为邻近因子合计占指数权重的31%。FSI值范围为0.30至0.91（均值 = 0.67），其中42.7%的绿洲面积（约1,707公顷）被归类为高易发性（FSI ≥ 0.7）。针对2021年8月火灾事件（318公顷）的验证表明模型具有较强的预测性能：96.5%的过火像元落在高风险区内，过火像元的FSI均值（0.72）显著高于未过火像元（0.67）（p \u003C 0.001），AUC为0.703。对60条历史火灾记录（2009–2024年）的佐证验证显示，98%的事件发生在高或极高易发性区域内。由此生成的FSI图为优先开展可燃物管理干预和火灾预防策略提供了可靠的空间依据。将其整合至动态地理数据库可进一步支持面向这些不可替代的绿洲景观的业务化野火预测预警系统（EWS）的开发。",null,"Discover Geoscience","2026-09-22T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"方法扎实、验证充分的遥感野火易发性研究，对干旱绿洲农业防灾有参考价值，但属区域性案例，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","防灾减灾","遥感","野火风险","绿洲农业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":64},[39,47,56],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":46},"I3",15,true,[44],"remote sensing",[29,44],[29],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":55},"H7",11,[],[52,53,54],"气候变化","干旱","climate change",[30,28],{"code":57,"score":58,"supported":42,"title":59,"abstract":60,"tags":63},"I2",7,[],[61,62],"深度学习","deep learning",[27],"农业相关性需要复核",[66,67],"摩洛哥 Ziz河谷 绿洲 野火","遥感 野火易发性 评估","摩洛哥Ziz河谷绿洲野火-3509",3,"10.1007\u002Fs44288-026-00735-8",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":81,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7213984877",[74,77,79],{"name":75,"orcid":76},"Rachid Ouachoua","https:\u002F\u002Forcid.org\u002F0000-0002-3194-7585",{"name":78,"orcid":9},"Hamid Benssi",{"name":80,"orcid":9},"Najib Kadiri",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00735-8.pdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"构建遥感与多准则决策融合的绿洲野火易发性指数，并验证其预测精度。","AHP加权线性组合，结合LST、NDVI、NDMI、道路居民点及U-Net土地覆","42.7%绿洲面积高易发，96.5%过火区落于高风险区，AUC达0.703。","农业遥感与作物表型","可延伸至动态地理数据库与实时早期预警系统，融合气象与人类活动数据。","openalex","2026-09-25T23:30:35.200699Z",{"total":92,"page":22,"page_size":92,"items":93},6,[94,170,230,299,352,464],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":108,"tags":110,"primary_topic":9,"secondary_topics":113,"classification":114,"search_phrases":141,"slug":144,"view_count":81,"doi":145,"paper":146,"created_at":169},4880,"Development of an Explainable GeoAI Wildfire Risk Analysis Model Using Multi-Source Geospatial Data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026","Accounting for approximately 31% of the global land surface, forests play a critical and indispensable role in sustaining ecological balance. Among the various pressures affecting forests, forest fires constitute a major environmental problem, posing significant threats to natural ecosystems while disrupting forestry activities and leading to substantial economic losses. In Türkiye, forest fires are predominantly observed in the Aegean, Mediterranean, and Marmara regions. This study presents a proactive, explainable GeoAI framework for wildfire risk analysis by integrating multi-source geospatial data. The methodology calculates a physical hazard index by evaluating advanced ensemble machine learning algorithm, specifically extreme gradient boosting (XGBoost) with the latter demonstrating superior predictive performance. The model is driven by topographic, meteorological, biotic and anthropogenic parameters. In parallel, a comprehensive vulnerability matrix encompassing physical, social, economic and environmental dimensions is constructed utilizing the Best-Worst Method (BWM). These two main components are spatially synthesized in a Geographic Information Systems (GIS) environment. The model's interpretability is enhanced focusing on feature importance to identify the primary drivers of fire ignition and spread. Results demonstrate that anthropogenic pressures and Wildland-Urban Interface (WUI) dynamics are the most significant risk determinants. The resulting high-resolution maps provide a robust Spatial Decision Support System (SDSS) for local authorities, enabling targeted mitigation strategies and contributing to the development of sustainable, smart and resilient cities.","森林约占全球陆地表面的31%，在维持生态平衡方面发挥着不可或缺的关键作用。在影响森林的诸多压力中，森林火灾构成重大环境问题，对自然生态系统造成严重威胁，同时干扰林业活动并导致重大经济损失。在土耳其，森林火灾主要发生于爱琴海、地中海和马尔马拉地区。本研究通过整合多源地理空间数据，提出了一种具有前瞻性和可解释性的地理人工智能（GeoAI）框架，用于野火风险分析。该方法通过评估先进的集成机器学习算法计算物理危险性指数，其中极端梯度提升（XGBoost）表现出更优的预测性能。模型由地形、气象、生物和人为参数驱动。与此同时，利用最优最劣法（BWM）构建了涵盖物理、社会、经济和环境维度的综合脆弱性矩阵。这两个主要组成部分在地理信息系统（GIS）环境中进行空间综合。通过聚焦特征重要性以识别火灾点燃和蔓延的主要驱动因素，增强了模型的可解释性。结果表明，人为压力和野地-城市交界区（WUI）动态是最重要的风险决定因素。所生成的高分辨率地图为地方当局提供了稳健的空间决策支持系统（SDSS），有助于制定有针对性的减缓策略，并促进可持续、智慧和韧性城市的发展。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences","2026-10-12T00:00:00Z",71,{"impact":17,"substance":104,"depth":105,"authority":20,"freshness":106,"relevant":22,"comment":107},20,17,9,"将可解释GeoAI与多源地理空间数据结合用于野火风险分析，方法新颖、结论明确，对农业与乡村防灾减灾有参考价值。",[109],{"name":100,"url":97},[27,28,29,111,112],"森林防火","地理信息系统",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":115},{"candidates":116,"agricultural_evidence":14,"reason":64},[117,124,132,137],{"code":57,"score":13,"supported":42,"title":118,"abstract":119,"tags":123},[],[120,121,122],"机器学习","machine learning","decision support",[27],{"code":125,"score":13,"supported":42,"title":126,"abstract":127,"tags":131},"I4",[],[128,129,130],"地理信息","GIS","geographic information",[112],{"code":48,"score":133,"supported":14,"title":134,"abstract":135,"tags":136},2,[],[],[111,28],{"code":40,"score":22,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[29],[142,143],"GeoAI 野火风险 XGBoost","土耳其 森林火灾 遥感","GeoAI野火风险XGBoost-4880","10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026",{"doi":145,"openalex_id":147,"authors":148,"venue":100,"cited_by_count":81,"oa_url":162,"card":163,"direction":168,"ingested_from":89},"W7221503678",[149,152,155,157,159],{"name":150,"orcid":151},"Esra Günaydın","https:\u002F\u002Forcid.org\u002F0000-0003-1868-0887",{"name":153,"orcid":154},"Muhammed Oğuzhan Mete","https:\u002F\u002Forcid.org\u002F0000-0002-9312-1965",{"name":156,"orcid":9},"Furkan Ali Kucuk",{"name":158,"orcid":9},"Ayça Hafızoğlu",{"name":160,"orcid":161},"Mahmut Oğuz Selbesoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-1132-3978","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F17\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-17-2026.pdf",{"tldr":164,"method":165,"finding":166,"direction":87,"opportunity":167},"构建可解释GeoAI框架，融合多源地理数据评估土耳其野火风险并制图。","XGBoost算危险指数，BWM建脆弱性矩阵，GIS空间合成。","人为压力与野火城市交界带动态是最显著的风险决定因素。","可将该可解释GeoAI框架迁移至农业火险与秸秆焚烧风险，结合时序遥感做动态预警。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.102337Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":9,"secondary_topics":186,"classification":187,"search_phrases":202,"slug":205,"view_count":92,"doi":206,"paper":207,"created_at":229},2956,"Integrating AI and Earth Observation Data for Disaster Risk Reduction","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02595-8","Integrating AI and Earth Observation Data for Disaster Risk Reduction。Journal of the Indian Society of Remote Sensing","将人工智能与地球观测数据相结合以降低灾害风险。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-18T00:00:00Z",62,{"impact":17,"substance":180,"depth":41,"authority":20,"freshness":21,"relevant":22,"comment":181},14,"AI与地球观测融合用于灾害风险降低的学术论文，与农业信息化相关但偏通用防灾，产业落地价值有限。",[183],{"name":176,"url":173},[27,28,29,185],"地球观测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":188},{"candidates":189,"agricultural_evidence":14,"reason":64},[190,194,198],{"code":40,"score":58,"supported":42,"title":191,"abstract":192,"tags":193},[],[29,44],[29],{"code":48,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[28],{"code":57,"score":22,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[27],[203,204],"AI 地球观测 灾害风险","农业人工智能 地球观测 防灾减灾 遥感","AI地球观测灾害风险-2956","10.1007\u002Fs12524-026-02595-8",{"doi":206,"openalex_id":208,"authors":209,"venue":176,"cited_by_count":81,"oa_url":222,"card":223,"direction":228,"ingested_from":89},"W7213552966",[210,213,216,219],{"name":211,"orcid":212},"Surajit Ghosh","https:\u002F\u002Forcid.org\u002F0000-0002-3928-2135",{"name":214,"orcid":215},"Md. Munsur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-9922-0374",{"name":217,"orcid":218},"Fasikaw A. Zimale","https:\u002F\u002Forcid.org\u002F0000-0001-9778-2712",{"name":220,"orcid":221},"Rajib Shaw","https:\u002F\u002Forcid.org\u002F0000-0003-3153-1800","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12524-026-02595-8.pdf",{"tldr":224,"method":225,"finding":226,"direction":87,"opportunity":227},"综述AI与地球观测数据融合用于灾害风险减少的研究进展。","综述AI与地球观测数据融合方法。","AI与地球观测融合可提升灾害风险监测与评估能力。","可探索AI与遥感融合在农业灾害风险预警与保险中的具体应用。","数字乡村与农业信息化","2026-09-19T23:30:40.818538Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":241,"tags":243,"primary_topic":247,"secondary_topics":252,"classification":253,"search_phrases":282,"slug":285,"view_count":81,"doi":286,"paper":287,"created_at":298},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":240},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[242],{"name":236,"url":233},[244,27,245,29,246],"智慧农业","粮食安全","气候分析",{"code":57,"slug":248,"name":249,"path":250,"parent_code":251},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":254,"locked":14,"version":35,"method":36,"evidence":255},"classified",{"candidates":256,"agricultural_evidence":42},[257,262,268,273,278],{"code":57,"score":106,"supported":42,"title":258,"abstract":260,"tags":261},[259],"artificial intelligence",[],[27],{"code":263,"score":69,"supported":42,"title":264,"abstract":265,"tags":267},"I5",[],[266],"农业传感",[],{"code":269,"score":22,"supported":14,"title":270,"abstract":271,"tags":272},"A8",[],[],[245],{"code":274,"score":22,"supported":14,"title":275,"abstract":276,"tags":277},"I1",[],[],[244],{"code":40,"score":22,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[29],[283,284],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":286,"openalex_id":288,"authors":289,"venue":236,"cited_by_count":81,"oa_url":233,"card":292,"direction":297,"ingested_from":89},"W7223111766",[290],{"name":291,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":293,"method":294,"finding":295,"direction":87,"opportunity":296},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":236,"source_url":302,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":308,"tags":310,"primary_topic":313,"secondary_topics":314,"classification":319,"search_phrases":336,"slug":339,"view_count":81,"doi":340,"paper":341,"created_at":351},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":21,"substance":104,"depth":105,"authority":17,"freshness":13,"relevant":22,"comment":307},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[309],{"name":236,"url":302},[244,27,311,29,312],"产量预测","可解释性",{"code":57,"slug":248,"name":249,"path":250,"parent_code":251},[315],{"code":40,"slug":316,"name":317,"path":318,"parent_code":251},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":254,"locked":14,"version":35,"method":36,"evidence":320},{"candidates":321,"agricultural_evidence":42},[322,328,332],{"code":57,"score":17,"supported":42,"title":323,"abstract":325,"tags":327},[324],"computer vision",[326],"计算机视觉",[27],{"code":40,"score":21,"supported":42,"title":329,"abstract":330,"tags":331},[],[29,44],[311,29],{"code":274,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[244],[337,338],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":340,"openalex_id":342,"authors":343,"venue":236,"cited_by_count":81,"oa_url":302,"card":346,"direction":87,"ingested_from":89},"W7223114002",[344],{"name":345,"orcid":9},"Kadiatou Barry",{"tldr":347,"method":348,"finding":349,"direction":87,"opportunity":350},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":362,"tags":364,"primary_topic":367,"secondary_topics":373,"classification":379,"search_phrases":435,"slug":438,"view_count":81,"doi":439,"paper":440,"created_at":463},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",79,{"impact":19,"substance":18,"depth":19,"authority":17,"freshness":106,"relevant":22,"comment":361},"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。",[363],{"name":358,"url":355},[244,27,29,365,366],"作物水分胁迫","精准灌溉",{"code":368,"slug":369,"name":370,"path":371,"parent_code":372},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[374,375],{"code":57,"slug":248,"name":249,"path":250,"parent_code":251},{"code":263,"slug":376,"name":377,"path":378,"parent_code":251},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":254,"locked":14,"version":35,"method":36,"evidence":380},{"candidates":381,"agricultural_evidence":42},[382,387,391,398,404,408,415,422,429],{"code":368,"score":41,"supported":42,"title":383,"abstract":384,"tags":386},[369],[385,369],"灌溉",[366],{"code":57,"score":13,"supported":42,"title":388,"abstract":389,"tags":390},[],[120,121,259],[27],{"code":263,"score":106,"supported":42,"title":392,"abstract":393,"tags":397},[],[394,395,396],"物联网","internet of things","IoT",[],{"code":274,"score":58,"supported":42,"title":399,"abstract":400,"tags":403},[],[401,402],"数字孪生","digital twin",[244],{"code":40,"score":58,"supported":42,"title":405,"abstract":406,"tags":407},[],[29,44],[29],{"code":409,"score":92,"supported":42,"title":410,"abstract":411,"tags":414},"A2",[],[412,413],"小麦","wheat",[],{"code":416,"score":92,"supported":42,"title":417,"abstract":418,"tags":421},"A3",[],[419,420],"玉米","maize",[],{"code":423,"score":92,"supported":42,"title":424,"abstract":425,"tags":428},"A5",[],[426,427],"马铃薯","potato",[],{"code":430,"score":69,"supported":42,"title":431,"abstract":432,"tags":434},"B2",[],[433],"葡萄",[],[436,437],"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":439,"openalex_id":441,"authors":442,"venue":358,"cited_by_count":81,"oa_url":355,"card":458,"direction":168,"ingested_from":89},"W7223088921",[443,446,449,452,455],{"name":444,"orcid":445},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":447,"orcid":448},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":450,"orcid":451},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":453,"orcid":454},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":456,"orcid":457},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":459,"method":460,"finding":461,"direction":87,"opportunity":462},"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":465,"title":466,"url":467,"summary":468,"summary_zh":469,"content":9,"source_name":236,"source_url":467,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":470,"score_detail":471,"sources":474,"tags":476,"primary_topic":480,"secondary_topics":481,"classification":484,"search_phrases":512,"slug":515,"view_count":81,"doi":516,"paper":517,"created_at":527},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":19,"substance":104,"depth":472,"authority":17,"freshness":106,"relevant":22,"comment":473},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[475],{"name":236,"url":467},[27,477,29,478,479],"农业物联网","智慧灌溉","节水农业",{"code":368,"slug":369,"name":370,"path":371,"parent_code":372},[482,483],{"code":263,"slug":376,"name":377,"path":378,"parent_code":251},{"code":40,"slug":316,"name":317,"path":318,"parent_code":251},{"status":254,"locked":14,"version":35,"method":36,"evidence":485},{"candidates":486,"agricultural_evidence":42},[487,493,497,501,505],{"code":368,"score":488,"supported":42,"title":489,"abstract":490,"tags":492},19,[369],[385,491,369],"水资源",[478,479],{"code":263,"score":19,"supported":42,"title":494,"abstract":495,"tags":496},[396],[394,395,396],[477],{"code":40,"score":41,"supported":42,"title":498,"abstract":499,"tags":500},[44],[29,44],[29],{"code":57,"score":17,"supported":42,"title":502,"abstract":503,"tags":504},[259],[259],[27],{"code":506,"score":92,"supported":42,"title":507,"abstract":508,"tags":511},"H8",[],[509,510],"可持续农业","sustainable agriculture",[],[513,514],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":516,"openalex_id":518,"authors":519,"venue":236,"cited_by_count":81,"oa_url":467,"card":522,"direction":168,"ingested_from":89},"W7223090389",[520],{"name":521,"orcid":9},"Patil",{"tldr":523,"method":524,"finding":525,"direction":168,"opportunity":526},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]