[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3772":3,"related-3772":115},{"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":90,"slug":93,"view_count":21,"doi":94,"paper":95,"created_at":114},3772,"Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought","https:\u002F\u002Fdoi.org\u002F10.1080\u002F02626667.2026.2740781","Agricultural drought threatens food security across Nigeria’s Sahel, yet spatially explicit machine-learning-based prediction remains limited in the region. This study developed a spatiotemporal drought monitoring and prediction framework for Yobe State (2000–2025) integrating seven remote-sensing indicators; NDVI, EVI, VCI, TCI, SMI, LST, and rainfall within Google Earth Engine at 3 km resolution. A persistent north–south drought gradient emerged across all indicators, with northern local government areas exhibiting chronic vegetation stress, soil moisture deficit, and elevated surface temperatures throughout the study period. Three machine learning models; LSTM, Random Forest, and XGBoost were trained on Vegetation Health Index-derived drought severity classes. XGBoost achieved the highest predictive performance (accuracy = 86.9%; Kappa = 0.829; RMSE = 2.26), with rainfall and vegetation condition as dominant predictors. All three models converge on the northern LGAs as the highest-risk zone by 2030, supporting targeted early warning deployment and climate-smart agricultural investment across the Sahel.","农业干旱威胁着尼日利亚萨赫勒地区的粮食安全，但该地区基于机器学习且具有空间显式特征的预测研究仍然有限。本研究在Google Earth Engine平台上，以3 km分辨率构建了约贝州（2000—2025年）的时空干旱监测与预测框架，集成了七种遥感指标：归一化植被指数（NDVI）、增强植被指数（EVI）、植被条件指数（VCI）、温度条件指数（TCI）、土壤水分指数（SMI）、地表温度（LST）和降雨量。所有指标均呈现出持续的南北干旱梯度，北部地方政府辖区在整个研究期内表现出长期的植被胁迫、土壤水分亏缺和地表温度升高。研究以植被健康指数（VHI）衍生的干旱严重程度等级为标签，训练了三种机器学习模型：长短期记忆网络（LSTM）、随机森林（Random Forest）和极端梯度提升（XGBoost）。XGBoost取得了最高的预测性能（准确率=86.9%；Kappa=0.829；RMSE=2.26），其中降雨量和植被状况为主要预测因子。三种模型一致预测，到2030年北部地方政府辖区将成为最高风险区，这为萨赫勒地区有针对性的预警部署和气候智慧型农业投资提供了依据。",null,"Hydrological Sciences Journal","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于多源遥感与机器学习构建萨赫勒地区农业干旱早期预警框架，方法扎实、结论明确，对干旱监测预警有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","机器学习","遥感监测","农业干旱","预警系统",{"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},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63,71,78,85],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},15,true,[54],"machine learning",[27,54],[27],{"code":39,"score":19,"supported":52,"title":58,"abstract":60,"tags":62},[59],"drought",[61,59],"干旱",[],{"code":64,"score":65,"supported":52,"title":66,"abstract":67,"tags":70},"A8",6,[],[68,69],"粮食安全","food security",[],{"code":72,"score":65,"supported":52,"title":73,"abstract":74,"tags":77},"H2",[],[75,76],"土壤","soil",[],{"code":79,"score":80,"supported":52,"title":81,"abstract":82,"tags":84},"I3",4,[],[83],"遥感",[28],{"code":86,"score":21,"supported":14,"title":87,"abstract":88,"tags":89},"I1",[],[],[26],[91,92],"尼日利亚 萨赫勒 农业干旱","XGBoost 植被健康指数 干旱预测","尼日利亚萨赫勒农业干旱-3772","10.1080\u002F02626667.2026.2740781",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":105,"oa_url":9,"card":106,"direction":112,"ingested_from":113},"W7214639469",[98,101,103],{"name":99,"orcid":100},"Gbolahan S. Badru","https:\u002F\u002Forcid.org\u002F0000-0003-4020-6356",{"name":102,"orcid":9},"Shakirudeen Odunuga",{"name":104,"orcid":9},"Michael Adebisi Adeyemi",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"基于多源遥感与机器学习构建尼日利亚萨赫勒地区农业干旱早期预测框架。","GEE整合NDVI、EVI、VCI等7个指标，用LSTM、随机森林、XGBoos","XGBoost预测精度最高（86.9%），北部LGAs为2030年前最高风险区。","农业遥感与作物表型","可探索多模型集成与迁移学习，将干旱预警框架推广至其他萨赫勒地区并耦合作物产量预测。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:09.236357Z",{"total":65,"page":21,"page_size":65,"items":116},[117,209,291,370,434,515],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":129,"tags":131,"primary_topic":132,"secondary_topics":136,"classification":139,"search_phrases":184,"slug":187,"view_count":188,"doi":189,"paper":190,"created_at":208},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":17,"substance":18,"depth":127,"authority":19,"freshness":20,"relevant":21,"comment":128},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[130],{"name":123,"url":120},[26,68,27,28,29],{"code":79,"slug":133,"name":134,"path":135,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[137,138],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":140},{"candidates":141,"agricultural_evidence":52},[142,147,151,158,163,167,174,180],{"code":79,"score":51,"supported":52,"title":143,"abstract":145,"tags":146},[144],"remote sensing",[83,144],[28],{"code":39,"score":19,"supported":52,"title":148,"abstract":149,"tags":150},[59],[61,59],[],{"code":32,"score":13,"supported":52,"title":152,"abstract":153,"tags":157},[],[154,27,54,155,156],"深度学习","deep learning","artificial intelligence",[27],{"code":64,"score":159,"supported":52,"title":160,"abstract":161,"tags":162},7,[],[68,69],[68],{"code":72,"score":65,"supported":52,"title":164,"abstract":165,"tags":166},[],[75,76],[],{"code":168,"score":169,"supported":52,"title":170,"abstract":171,"tags":173},"D",3,[],[172],"水产",[],{"code":175,"score":169,"supported":52,"title":176,"abstract":177,"tags":179},"H6",[],[178],"农业生态",[],{"code":86,"score":21,"supported":14,"title":181,"abstract":182,"tags":183},[],[],[26],[185,186],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":189,"openalex_id":191,"authors":192,"venue":123,"cited_by_count":105,"oa_url":202,"card":203,"direction":112,"ingested_from":113},"W7221081979",[193,196,199],{"name":194,"orcid":195},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":197,"orcid":198},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":200,"orcid":201},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":204,"method":205,"finding":206,"direction":110,"opportunity":207},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":223,"tags":225,"primary_topic":229,"secondary_topics":230,"classification":232,"search_phrases":274,"slug":277,"view_count":21,"doi":278,"paper":279,"created_at":290},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":219,"depth":51,"authority":220,"freshness":221,"relevant":21,"comment":222},16,12,9,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[224],{"name":215,"url":212},[26,226,227,228,28],"农业人工智能","可持续发展","气候变化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[231],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":233},{"candidates":234,"agricultural_evidence":52},[235,239,244,250,254,261,267],{"code":32,"score":17,"supported":52,"title":236,"abstract":237,"tags":238},[156],[154,27,54,155,156],[226],{"code":39,"score":51,"supported":52,"title":240,"abstract":242,"tags":243},[241],"climate change",[228,241],[228],{"code":86,"score":159,"supported":52,"title":245,"abstract":246,"tags":249},[],[247,248],"精准农业","precision agriculture",[26],{"code":79,"score":159,"supported":52,"title":251,"abstract":252,"tags":253},[],[83,144],[28],{"code":255,"score":65,"supported":52,"title":256,"abstract":257,"tags":260},"H4",[],[258,259],"碳排放","carbon emission",[],{"code":175,"score":65,"supported":52,"title":262,"abstract":263,"tags":266},[],[264,265],"生物多样性","biodiversity",[],{"code":268,"score":65,"supported":52,"title":269,"abstract":270,"tags":273},"I5",[],[271,272],"物联网","internet of things",[],[275,276],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":278,"openalex_id":280,"authors":281,"venue":215,"cited_by_count":105,"oa_url":212,"card":284,"direction":112,"ingested_from":113},"W7222092855",[282],{"name":283,"orcid":9},"Deepmala Suryavanshi",{"tldr":285,"method":286,"finding":287,"direction":288,"opportunity":289},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":308,"secondary_topics":311,"classification":314,"search_phrases":337,"slug":340,"view_count":188,"doi":341,"paper":342,"created_at":369},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":219,"substance":18,"depth":17,"authority":19,"freshness":221,"relevant":21,"comment":301},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[303],{"name":297,"url":294},[27,305,306,28,307],"土壤有机碳","生态修复","干旱区农业",{"code":72,"slug":76,"name":309,"path":310,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[312,313],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":315},{"candidates":316,"agricultural_evidence":52},[317,321,325,329,333],{"code":72,"score":51,"supported":52,"title":318,"abstract":319,"tags":320},[76],[75,76],[305],{"code":39,"score":13,"supported":52,"title":322,"abstract":323,"tags":324},[],[228,61,241],[307],{"code":32,"score":221,"supported":52,"title":326,"abstract":327,"tags":328},[54],[],[27],{"code":79,"score":159,"supported":52,"title":330,"abstract":331,"tags":332},[],[83,144],[28],{"code":175,"score":21,"supported":14,"title":334,"abstract":335,"tags":336},[],[],[306],[338,339],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":341,"openalex_id":343,"authors":344,"venue":297,"cited_by_count":105,"oa_url":294,"card":364,"direction":110,"ingested_from":113},"W7219075005",[345,347,350,352,355,357,360,362],{"name":346,"orcid":9},"Y. Chen",{"name":348,"orcid":349},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":351,"orcid":9},"Meng Zhu",{"name":353,"orcid":354},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":356,"orcid":9},"Yulian Hao",{"name":358,"orcid":359},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":361,"orcid":9},"Zhehui Wang",{"name":363,"orcid":9},"Bin Wang",{"tldr":365,"method":366,"finding":367,"direction":110,"opportunity":368},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":383,"tags":385,"primary_topic":387,"secondary_topics":388,"classification":390,"search_phrases":413,"slug":416,"view_count":188,"doi":417,"paper":418,"created_at":433},2151,"An adaptive deep learning framework for multi-temporal crop and drought stress monitoring in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70304-z","Precision agriculture increasingly relies on data-driven methods to address the challenges of crop stress and drought monitoring under changing climatic conditions. Hence, a deep learning (DL) based innovative framework is required for assessing crop stress dynamics, aimed at improving agricultural productivity and ensuring regional food security, because conventional models often struggle to capture subtle spatial and spectral variations, resulting in limited accuracy and generalizability. To overcome these challenges, an innovative and adaptive DL-based approach has been proposed to utilize multi-temporal Sentinel-2 satellite images collected over Haldharmau village, Gonda District, Uttar Pradesh. The model effectively integrates local spectral and spatial details with broader contextual features, enabling crop classification and stress detection to support precision agriculture. The crop stress and drought maps have been generated with the help of a CNN+ViT model (i.e., A hybrid DL-based classification model) based classified images in conjunction with NDVI and NDWI images to evaluate drought-induced crop variability across 2023, 2024, and 2025. The monthly (January to June) stress maps derived from this process provide valuable insights into temporal patterns of crop exposure. The proposed framework offers quantitative insights into temporal and spatial patterns of crop vulnerability and resilience across semi‐arid agricultural landscapes. Therefore, the proposed framework advances precision agriculture modeling by integrating satellite images, deep learning, and phenological analysis, and is readily transferable to other drought-prone regions with analogous crop systems.","精准农业日益依赖数据驱动方法来应对气候变化条件下作物胁迫与干旱监测的挑战。因此，需要一种基于深度学习（DL）的创新框架来评估作物胁迫动态，以提高农业生产力并保障区域粮食安全，因为传统模型往往难以捕捉细微的空间和光谱变化，导致精度和泛化能力有限。为克服这些挑战，提出了一种创新且自适应的基于深度学习的方法，利用在北方邦戈达县哈尔达毛村采集的多时相Sentinel-2卫星影像。该模型有效整合了局部光谱与空间细节以及更广泛的上下文特征，实现了作物分类和胁迫检测，以支持精准农业。作物胁迫和干旱地图借助CNN+ViT模型（即基于混合深度学习的分类模型）生成的分类影像，结合NDVI和NDWI影像，评估了2023年、2024年和2025年干旱引起的作物变异性。通过该过程得到的逐月（1月至6月）胁迫地图为作物暴露的时间格局提供了有价值的见解。所提出的框架为半干旱农业景观中作物脆弱性和恢复力的时空格局提供了定量认识。因此，该框架通过整合卫星影像、深度学习和物候分析，推进了精准农业建模，并可随时推广至具有类似作物系统的其他干旱易发地区。","Scientific Reports","2026-09-09T00:00:00Z",76,{"impact":219,"substance":380,"depth":381,"authority":19,"freshness":20,"relevant":21,"comment":382},21,17,"基于CNN+ViT混合模型与Sentinel-2多时相影像的作物分类与干旱胁迫监测框架，方法新颖、数据跨三年，对旱区精准农业有参考价值。",[384],{"name":376,"url":373},[26,226,247,28,386],"作物干旱胁迫",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[389],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":391},{"candidates":392,"agricultural_evidence":52},[393,397,401,405,409],{"code":39,"score":381,"supported":52,"title":394,"abstract":395,"tags":396},[59],[228,61,59],[],{"code":86,"score":219,"supported":52,"title":398,"abstract":399,"tags":400},[248],[247,248],[26,247],{"code":32,"score":51,"supported":52,"title":402,"abstract":403,"tags":404},[155],[154,155],[226],{"code":64,"score":65,"supported":52,"title":406,"abstract":407,"tags":408},[],[68,69],[],{"code":79,"score":21,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[28],[414,415],"作物干旱胁迫 农业人工智能 智慧农业 精准农业","作物干旱胁迫 农业人工智能","作物干旱胁迫农业人工智能智慧农业精准农业-2151","10.1038\u002Fs41598-026-70304-z",{"doi":417,"openalex_id":419,"authors":420,"venue":376,"cited_by_count":105,"oa_url":427,"card":428,"direction":112,"ingested_from":113},"W7212034680",[421,424],{"name":422,"orcid":423},"Gausiya Yasmeen","https:\u002F\u002Forcid.org\u002F0000-0002-7853-1376",{"name":425,"orcid":426},"Tasneem Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2702-3168","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70304-z_reference.pdf",{"tldr":429,"method":430,"finding":431,"direction":110,"opportunity":432},"提出CNN+ViT混合深度学习框架，利用多时相Sentinel-2影像监测作物分类与干旱胁迫。","CNN+ViT混合模型结合NDVI、NDWI，基于Sentinel-2多时相影像","框架可量化半干旱区作物时空脆弱性与恢复力，生成月度胁迫图。","可探索多源遥感与时序模型融合，提升跨区域干旱胁迫预警的泛化能力。","2026-09-11T23:30:13.461760Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":442,"sources":446,"tags":448,"primary_topic":452,"secondary_topics":453,"classification":459,"search_phrases":491,"slug":494,"view_count":188,"doi":495,"paper":496,"created_at":514},4656,"Artificial Intelligence, Foundation Models, and Digital Twins for Climate-Resilient Agriculture: A Review","https:\u002F\u002Fdoi.org\u002F10.54963\u002Fia.v2i2.103039","Climate change, increasing food demand, and resource constraints are accelerating the transition from conventional precision agriculture toward smart, autonomous, and climate-resilient agricultural systems. Artificial intelligence (AI), foundation models, digital twins (DTs), the Internet of Things (IoT), remote sensing, edge–cloud computing, and big data analytics are emerging as key technologies for data-driven agricultural management. This review critically evaluates recent advances in AI-driven intelligent agriculture, with emphasis on machine learning, deep learning, explainable AI (XAI), generative and agentic AI, multimodal foundation models, and digital twins, together with the sensing and computing infrastructures required for their deployment. Relevant literature was identified using keywords related to AI, foundation models, digital twins, IoT, precision agriculture, and climate-resilient agriculture, with emphasis on recent peer-reviewed studies addressing technological applications, performance, limitations, challenges, and future prospects. The selected literature was critically synthesized to identify technological trends, knowledge gaps, and emerging research opportunities. The review highlights that the greatest potential of intelligent agriculture lies in integrating heterogeneous data from sensors, UAVs (unmanned aerial vehicles), satellites, weather systems, field operations, and agricultural knowledge to support context-aware prediction, decision-making, and autonomous action. However, data quality, model generalizability, explainability, interoperability, cybersecurity, computational requirements, and large-scale field validation remain major challenges. This review further develops an integrated perspective connecting AI, foundation models, multimodal data infrastructures, digital twins, and autonomous agricultural systems within a unified cyber-physical ecosystem. Future priorities include trustworthy AI, agentic decision-making, semantic interoperability, federated learning, continuous learning, and self-learning digital twins toward Agriculture 6.0.","气候变化、粮食需求增长与资源约束正加速推动传统精准农业向智能、自主且气候适应型的农业系统转型。人工智能（AI）、基础模型、数字孪生（DT）、物联网（IoT）、遥感、边缘—云计算和大数据分析正成为数据驱动农业管理的关键技术。本综述批判性地评估了人工智能驱动智能农业的最新进展，重点关注机器学习、深度学习、可解释人工智能（XAI）、生成式与代理式人工智能、多模态基础模型和数字孪生，以及其部署所需的传感与计算基础设施。相关文献通过人工智能、基础模型、数字孪生、物联网、精准农业和气候适应型农业等关键词检索获得，重点纳入近期同行评审研究，涵盖技术应用、性能、局限性、挑战与未来前景。对所选文献进行了批判性综合，以识别技术趋势、知识空白和新兴研究机遇。本综述强调，智能农业的最大潜力在于整合来自传感器、无人机（UAV）、卫星、气象系统、田间作业和农业知识的异构数据，以支持情境感知预测、决策和自主行动。然而，数据质量、模型泛化能力、可解释性、互操作性、网络安全、计算需求和大规模田间验证仍是主要挑战。本综述进一步提出了一种整合视角，将人工智能、基础模型、多模态数据基础设施、数字孪生和自主农业系统连接于统一的网络物理生态系统中。未来优先方向包括可信人工智能、代理式决策、语义互操作性、联邦学习、持续学习以及面向农业6.0的自学习数字孪生。","Intelligent Agriculture","2026-10-10T00:00:00Z",{"impact":18,"substance":443,"depth":17,"authority":444,"freshness":20,"relevant":21,"comment":445},20,13,"系统综述AI、基础模型与数字孪生在气候韧性农业中的集成路径与挑战，学术价值高，对智慧农业方向有较强参考意义。",[447],{"name":440,"url":437},[26,226,449,450,451],"农业大模型","数字孪生","气候韧性农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[454,455],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":268,"slug":456,"name":457,"path":458,"parent_code":36},"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":460},{"candidates":461,"agricultural_evidence":52},[462,466,471,476,481,485],{"code":32,"score":127,"supported":52,"title":463,"abstract":464,"tags":465},[156],[154,27,54,155,156],[226,449],{"code":86,"score":467,"supported":52,"title":468,"abstract":469,"tags":470},11,[],[247,450,248],[450,26],{"code":39,"score":13,"supported":52,"title":472,"abstract":473,"tags":475},[],[228,474,241],"气候适应",[451],{"code":268,"score":221,"supported":52,"title":477,"abstract":478,"tags":480},[],[271,272,479],"IoT",[],{"code":79,"score":65,"supported":52,"title":482,"abstract":483,"tags":484},[],[83,144],[],{"code":486,"score":169,"supported":52,"title":487,"abstract":488,"tags":490},"A7",[],[489],"间作",[],[492,493],"农业基础模型 数字孪生","气候韧性农业 人工智能","农业基础模型数字孪生-4656","10.54963\u002Fia.v2i2.103039",{"doi":495,"openalex_id":497,"authors":498,"venue":440,"cited_by_count":105,"oa_url":437,"card":508,"direction":112,"ingested_from":113},"W7221321372",[499,502,505],{"name":500,"orcid":501},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":503,"orcid":504},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":506,"orcid":507},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":509,"method":510,"finding":511,"direction":512,"opportunity":513},"综述AI、基础模型与数字孪生如何支撑气候韧性农业，并指出关键挑战与未来方向。","文献综述，聚焦机器学习、深度学习、可解释AI、多模态基础模型与数字孪生。","最大潜力在于融合多源异构数据实现情境感知预测与自主决策，但数据质量与可解释性等仍是瓶颈。","农业人工智能与决策模型","可探索可信AI、联邦学习与自学习数字孪生在真实农田的规模化验证与语义互操作。","2026-10-11T23:30:07.465569Z",{"id":516,"title":517,"url":518,"summary":519,"summary_zh":520,"content":9,"source_name":521,"source_url":518,"published_at":522,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":523,"sources":525,"tags":527,"primary_topic":529,"secondary_topics":530,"classification":533,"search_phrases":576,"slug":579,"view_count":188,"doi":580,"paper":581,"created_at":594},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":221,"relevant":21,"comment":524},"提出农业数字孪生与GRU融合的实时产量预测与决策支持框架，方法新颖、指标扎实，对智慧农业研究有参考价值。",[526],{"name":521,"url":518},[26,226,271,450,528],"作物产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[531,532],{"code":268,"slug":456,"name":457,"path":458,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":534},{"candidates":535,"agricultural_evidence":52},[536,542,547,551,555,559,566,572],{"code":86,"score":127,"supported":52,"title":537,"abstract":539,"tags":541},[538],"digital twin",[26,450,540,538],"smart agriculture",[450,26],{"code":32,"score":220,"supported":52,"title":543,"abstract":545,"tags":546},[544],"decision support",[156],[226],{"code":268,"score":13,"supported":52,"title":548,"abstract":549,"tags":550},[],[271,272,479],[271],{"code":39,"score":221,"supported":52,"title":552,"abstract":553,"tags":554},[],[228,474,241],[],{"code":72,"score":65,"supported":52,"title":556,"abstract":557,"tags":558},[],[75,76],[],{"code":560,"score":65,"supported":52,"title":561,"abstract":562,"tags":565},"H3",[],[563,564],"灌溉","irrigation",[],{"code":567,"score":169,"supported":52,"title":568,"abstract":569,"tags":571},"H8",[],[570],"可持续农业",[],{"code":79,"score":21,"supported":14,"title":573,"abstract":574,"tags":575},[],[],[528],[577,578],"Agri Digital Twin GRU 产量预测","物联网 数字孪生 智慧农业","AgriDigitalTwinGRU产量预测-4213","10.1016\u002Fj.engappai.2026.116418",{"doi":580,"openalex_id":582,"authors":583,"venue":521,"cited_by_count":105,"oa_url":518,"card":589,"direction":112,"ingested_from":113},"W7219849006",[584,587],{"name":585,"orcid":586},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":588,"orcid":9},"Munish Bhatia",{"tldr":590,"method":591,"finding":592,"direction":112,"opportunity":593},"提出农业数字孪生框架，结合GRU与物联网实现实时产量预测与可持续决策支持。","基于物联网传感实时采集土壤气象数据，用GRU模型做时序产量预测与分类。","预测MAE 0.0243、R² 0.9254，平均准确率97.41%，优于BiLSTM、LSTM和1","可探索多源遥感与数字孪生融合、跨区域迁移学习及决策闭环验证等空白。","2026-10-05T23:30:09.119891Z"]