[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5086":3,"related-5086":123},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":103,"slug":106,"view_count":22,"doi":107,"paper":108,"created_at":122},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,16,15,12,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","可持续发展","气候变化","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,61,66,74,81,89,96],{"code":33,"score":17,"supported":52,"title":53,"abstract":55,"tags":60},true,[54],"artificial intelligence",[56,57,58,59,54],"深度学习","机器学习","machine learning","deep learning",[28],{"code":40,"score":19,"supported":52,"title":62,"abstract":64,"tags":65},[63],"climate change",[30,63],[30],{"code":67,"score":68,"supported":52,"title":69,"abstract":70,"tags":73},"I1",7,[],[71,72],"精准农业","precision agriculture",[27],{"code":75,"score":68,"supported":52,"title":76,"abstract":77,"tags":80},"I3",[],[78,79],"遥感","remote sensing",[31],{"code":82,"score":83,"supported":52,"title":84,"abstract":85,"tags":88},"H4",6,[],[86,87],"碳排放","carbon emission",[],{"code":90,"score":83,"supported":52,"title":91,"abstract":92,"tags":95},"H6",[],[93,94],"生物多样性","biodiversity",[],{"code":97,"score":83,"supported":52,"title":98,"abstract":99,"tags":102},"I5",[],[100,101],"物联网","internet of things",[],[104,105],"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":107,"openalex_id":109,"authors":110,"venue":10,"cited_by_count":113,"oa_url":6,"card":114,"direction":120,"ingested_from":121},"W7222092855",[111],{"name":112,"orcid":9},"Deepmala Suryavanshi",0,{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:15.059867Z",{"total":83,"page":22,"page_size":83,"items":124},[125,190,263,335,422,502],{"id":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":138,"tags":140,"primary_topic":142,"secondary_topics":143,"classification":145,"search_phrases":167,"slug":170,"view_count":171,"doi":172,"paper":173,"created_at":189},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures","2026-09-23T00:00:00Z",80,{"impact":17,"substance":135,"depth":17,"authority":136,"freshness":21,"relevant":22,"comment":137},22,13,"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[139],{"name":131,"url":128},[27,28,29,30,141],"文献计量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[144],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":146},{"candidates":147,"agricultural_evidence":52},[148,152,156,162],{"code":33,"score":17,"supported":52,"title":149,"abstract":150,"tags":151},[54],[56,57,58,59,54],[28],{"code":40,"score":19,"supported":52,"title":153,"abstract":154,"tags":155},[63],[30,63],[30],{"code":157,"score":19,"supported":52,"title":158,"abstract":160,"tags":161},"I7",[159],"bibliometric",[141,159],[141],{"code":67,"score":68,"supported":52,"title":163,"abstract":164,"tags":166},[],[27,165],"smart agriculture",[27],[168,169],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353",5,"10.1016\u002Fj.sftr.2026.102145",{"doi":172,"openalex_id":174,"authors":175,"venue":131,"cited_by_count":113,"oa_url":128,"card":183,"direction":120,"ingested_from":121},"W7214099405",[176,179,181],{"name":177,"orcid":178},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":180,"orcid":9},"Shiyan Zhai",{"name":182,"orcid":9},"Yuke Feng",{"tldr":184,"method":185,"finding":186,"direction":187,"opportunity":188},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","农业人工智能与决策模型","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":202,"tags":204,"primary_topic":206,"secondary_topics":207,"classification":209,"search_phrases":240,"slug":242,"view_count":22,"doi":243,"paper":244,"created_at":262},2642,"Artificial Intelligence for Environmental Resilience and Sustainable Innovation","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.239","Environmental challenges such as climate change, resource depletion, biodiversity loss, and increasing pollution require innovative and adaptive solutions to strengthen environmental resilience. Artificial intelligence (AI) has emerged as a transformative technology capable of improving environmental monitoring, prediction, and decision-making processes. This study examines the role of artificial intelligence in enhancing environmental resilience and promoting sustainable innovation across various sectors, including natural resource management, climate adaptation, waste management, environmental conservation, and smart environmental systems. This study adopts a Systematic Literature Review (SLR) methodology integrated with bibliometric analysis and thematic content analysis to identify research trends, conceptual developments, and emerging themes related to AI applications in environmental sustainability. The selected studies were analyzed using VOSviewer, Tableau, and Microsoft Excel to visualize knowledge structures, thematic relationships, and research patterns. The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making. Furthermore, AI-driven innovations support sustainable practices through renewable energy optimization, precision agriculture, waste management improvement, water resource monitoring, and smart city development. However, challenges remain regarding data quality, technological accessibility, computational requirements, ethical considerations, and responsible governance. The study concludes that AI has substantial potential to accelerate environmental resilience and sustainable innovation when supported by appropriate policies, stakeholder collaboration, and long-term sustainability strategies. By integrating intelligent technologies with responsible environmental management approaches, AI can serve as a strategic enabler in addressing complex ecological challenges and supporting a more adaptive, resilient, and sustainable future.","气候变化、资源枯竭、生物多样性丧失和污染加剧等环境挑战，需要创新性和适应性的解决方案来增强环境韧性。人工智能（AI）已成为一种变革性技术，能够改善环境监测、预测和决策过程。本研究探讨了人工智能在增强环境韧性和促进各领域可持续创新中的作用，涵盖自然资源管理、气候适应、废物管理、环境保护和智能环境系统等。本研究采用系统性文献综述（SLR）方法，并结合文献计量分析和主题内容分析，以识别与人工智能在环境可持续性中应用相关的研究趋势、概念发展和新兴主题。所选研究使用VOSviewer、Tableau和Microsoft Excel进行分析，以可视化知识结构、主题关系和研究模式。研究结果表明，人工智能通过实现先进数据分析、环境风险早期检测、预测性评估、资源优化和循证决策，显著促进了环境韧性。此外，人工智能驱动的创新通过可再生能源优化、精准农业、废物管理改进、水资源监测和智慧城市发展，支持可持续实践。然而，在数据质量、技术可及性、计算需求、伦理考量和负责任治理方面仍存在挑战。研究结论认为，在适当的政策、利益相关者协作和长期可持续战略支持下，人工智能具有加速环境韧性和可持续创新的巨大潜力。通过将智能技术与负责任的环境管理方法相结合，人工智能可以作为应对复杂生态挑战、支持更具适应性、韧性和可持续未来的战略赋能者。","AI Innovation and Resilience for the Environment (AIR)","2026-09-15T00:00:00Z",72,{"impact":17,"substance":17,"depth":18,"authority":20,"freshness":200,"relevant":22,"comment":201},8,"系统综述类论文，梳理AI在环境韧性与可持续创新中的应用，涵盖精准农业、水资源监测等方向，方法规范但结论偏综述性，对农业信息化有参考价值。",[203],{"name":196,"url":193},[27,28,29,71,205],"环境韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[208],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":210},{"candidates":211,"agricultural_evidence":52},[212,216,221,225,232,236],{"code":33,"score":20,"supported":52,"title":213,"abstract":214,"tags":215},[54],[54],[28],{"code":40,"score":21,"supported":52,"title":217,"abstract":218,"tags":220},[],[30,219,63],"气候适应",[],{"code":67,"score":200,"supported":52,"title":222,"abstract":223,"tags":224},[],[71,72],[27,71],{"code":226,"score":83,"supported":52,"title":227,"abstract":228,"tags":231},"H3",[],[229,230],"水资源","water resource",[],{"code":90,"score":83,"supported":52,"title":233,"abstract":234,"tags":235},[],[93,94],[],{"code":157,"score":83,"supported":52,"title":237,"abstract":238,"tags":239},[],[141,159],[],[241,169],"农业人工智能 可持续发展 智慧农业 环境韧性","农业人工智能可持续发展智慧农业环境韧性-2642","10.68012\u002Fair.v1i2.239",{"doi":243,"openalex_id":245,"authors":246,"venue":196,"cited_by_count":113,"oa_url":193,"card":257,"direction":120,"ingested_from":121},"W7213274173",[247,249,252,255],{"name":248,"orcid":9},"Sri Poedji Lestari",{"name":250,"orcid":251},"Rosa Lesmana","https:\u002F\u002Forcid.org\u002F0009-0002-5308-6023",{"name":253,"orcid":254},"Sabil Maulana Fauzi","https:\u002F\u002Forcid.org\u002F0009-0003-1833-1027",{"name":256,"orcid":9},"Alexander Johnson Johnson",{"tldr":258,"method":259,"finding":260,"direction":118,"opportunity":261},"系统综述AI在环境韧性与可持续创新中的应用、趋势与挑战。","系统文献综述结合文献计量与主题分析，用VOSviewer等可视化。","AI通过数据分析、风险预警和资源优化显著增强环境韧性，但存在数据与治理挑战。","可聚焦AI在精准农业与碳减排协同中的实证研究，弥补数据质量与治理机制空白。","2026-09-16T23:30:11.835233Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":277,"tags":279,"primary_topic":281,"secondary_topics":282,"classification":284,"search_phrases":312,"slug":315,"view_count":316,"doi":317,"paper":318,"created_at":334},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":18,"substance":273,"depth":274,"authority":275,"freshness":200,"relevant":22,"comment":276},21,17,14,"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。",[278],{"name":269,"url":266},[27,28,71,31,280],"作物干旱胁迫",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[283],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":285},{"candidates":286,"agricultural_evidence":52},[287,293,297,301,308],{"code":40,"score":274,"supported":52,"title":288,"abstract":290,"tags":292},[289],"drought",[30,291,289],"干旱",[],{"code":67,"score":18,"supported":52,"title":294,"abstract":295,"tags":296},[72],[71,72],[27,71],{"code":33,"score":19,"supported":52,"title":298,"abstract":299,"tags":300},[59],[56,59],[28],{"code":302,"score":83,"supported":52,"title":303,"abstract":304,"tags":307},"A8",[],[305,306],"粮食安全","food security",[],{"code":75,"score":22,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[31],[313,314],"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",2,"10.1038\u002Fs41598-026-70304-z",{"doi":317,"openalex_id":319,"authors":320,"venue":269,"cited_by_count":113,"oa_url":327,"card":328,"direction":120,"ingested_from":121},"W7212034680",[321,324],{"name":322,"orcid":323},"Gausiya Yasmeen","https:\u002F\u002Forcid.org\u002F0000-0002-7853-1376",{"name":325,"orcid":326},"Tasneem Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2702-3168","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70304-z_reference.pdf",{"tldr":329,"method":330,"finding":331,"direction":332,"opportunity":333},"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。","农业遥感与作物表型","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":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":349,"secondary_topics":350,"classification":352,"search_phrases":395,"slug":397,"view_count":22,"doi":398,"paper":399,"created_at":421},1979,"Advances in artificial intelligence and geomatics technology for the transformation of global agri-food systems: A review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43994-026-00357-3","Abstract The world’s rapidly growing population is driving up food demand and placing significant pressure on natural resources, posing major challenges to agricultural sustainability. Ensuring food security requires integrating innovative technologies that enhance productivity while reducing environmental impacts. In this context, the convergence of Artificial Intelligence (AI) and Geomatics Technology (GT) has emerged as a key approach for the intelligent management of agricultural systems, enabling improved monitoring, analysis, and modelling of both productive and environmental processes. This study aims to explore recent trends in the combined application of AI and GT in sustainable agriculture by systematically reviewing scientific publications retrieved from Scopus and Web of Science (WoS) covering 2004 to 2025. The review focuses on identifying sustainable agricultural practices and environmental strategies, and on their contributions to sectoral sustainability. The methodology integrates a bibliometric analysis of 326 publications with a PRISMA-based systematic review of 83 eligible studies, including an in-depth synthesis of the 40 studies classified under the Environmental Monitoring and Climate Change category. Bibliometric mapping was performed using VOSviewer, while quantitative evidence synthesis supported the regional comparative analyses. The results indicate a growing adoption of Machine Learning (ML) algorithms, remote sensing technologies, and geospatial platforms such as Google Earth Engine (GEE), highlighting AI and GT as central components of Smart Agriculture. Sustainable practices are primarily classified into Smart Agriculture (25%), Environmental Monitoring and Climate Change (48%), and Decision-Making processes (27%). Asia and North America lead in technological adoption, whereas Latin America and Oceania face limitations related to infrastructure and data accessibility. The integration of AI and GT enhances climate resilience, supports ecosystem conservation, and contributes to achieving the Sustainable Development Goals (SDGs). These findings provide a robust scientific and technological foundation for advancing the global transition towards more sustainable and adaptive agri-food systems.","全球人口的快速增长正在推动粮食需求上升，并对自然资源造成巨大压力，给农业可持续性带来重大挑战。确保粮食安全需要整合创新技术，在提高生产力的同时减少环境影响。在此背景下，人工智能（AI）与地理信息学技术（GT）的融合已成为农业系统智能化管理的关键途径，能够实现对生产及环境过程的改进监测、分析与建模。本研究旨在通过系统梳理从Scopus和Web of Science（WoS）检索到的2004年至2025年间科学文献，探索AI与GT在可持续农业中联合应用的最新趋势。综述重点在于识别可持续农业实践与环境策略，及其对行业可持续性的贡献。研究方法将326篇文献的文献计量分析与基于PRISMA的83项符合条件研究的系统综述相结合，并对归入“环境监测与气候变化”类别的40项研究进行了深入综合。文献计量图谱采用VOSviewer绘制，定量证据综合支持了区域比较分析。结果表明，机器学习（ML）算法、遥感技术和诸如Google Earth Engine（GEE）等地理空间平台的应用日益广泛，凸显出AI与GT作为智慧农业核心组成部分的地位。可持续实践主要分为智慧农业（25%）、环境监测与气候变化（48%）以及决策过程（27%）三类。亚洲和北美在技术采用方面处于领先地位，而拉丁美洲和大洋洲则面临基础设施与数据可获取性相关的限制。AI与GT的整合增强了气候韧性，支持了生态系统保护，并有助于实现可持续发展目标（SDGs）。这些发现为推进全球向更具可持续性和适应性的农食系统转型提供了坚实的科学与技术基础。","Journal of Umm Al-Qura University for Applied Sciences",71,{"impact":17,"substance":135,"depth":17,"authority":20,"freshness":22,"relevant":22,"comment":344},"系统综述AI与地学技术融合应用，覆盖2004-2025年326篇文献，为智慧农业提供科学依据，但时效性较低。",[346],{"name":341,"url":338},[27,28,29,348],"遥感技术",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[351],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":353},{"candidates":354,"agricultural_evidence":52},[355,359,364,369,373,377,381,388],{"code":33,"score":17,"supported":52,"title":356,"abstract":357,"tags":358},[54],[57,58,54],[28],{"code":40,"score":21,"supported":52,"title":360,"abstract":361,"tags":363},[],[30,63,362],"climate resilience",[],{"code":157,"score":21,"supported":52,"title":365,"abstract":366,"tags":368},[],[141,367,159],"研究方法",[],{"code":67,"score":68,"supported":52,"title":370,"abstract":371,"tags":372},[],[27,165],[27],{"code":75,"score":68,"supported":52,"title":374,"abstract":375,"tags":376},[],[78,79],[348],{"code":302,"score":83,"supported":52,"title":378,"abstract":379,"tags":380},[],[305,306],[],{"code":382,"score":83,"supported":52,"title":383,"abstract":384,"tags":387},"H8",[],[385,386],"可持续农业","sustainable agriculture",[],{"code":389,"score":390,"supported":52,"title":391,"abstract":392,"tags":394},"I4",3,[],[393],"地理信息",[],[396,169],"农业人工智能 可持续发展 智慧农业 遥感技术","农业人工智能可持续发展智慧农业遥感技术-1979","10.1007\u002Fs43994-026-00357-3",{"doi":398,"openalex_id":400,"authors":401,"venue":341,"cited_by_count":113,"oa_url":338,"card":416,"direction":120,"ingested_from":121},"W7211954581",[402,404,406,408,410,413],{"name":403,"orcid":9},"Paulo César Escandón Panchana",{"name":405,"orcid":9},"Aline Maria Meiguins de Lima",{"name":407,"orcid":9},"Andrés Velastegui-Montoya",{"name":409,"orcid":9},"Eddy Sanclemente Ordoñez",{"name":411,"orcid":412},"Fernanda Calderón","https:\u002F\u002Forcid.org\u002F0000-0002-5191-7527",{"name":414,"orcid":415},"Sandra Martínez Cuevas","https:\u002F\u002Forcid.org\u002F0000-0002-2150-3251",{"tldr":417,"method":418,"finding":419,"direction":187,"opportunity":420},"综述AI与地理空间技术在全球农业食品系统转型中的应用，分析2004-2025年文献，识别可持续农业实","系统综述Scopus和WoS文献，结合326篇文献计量分析与83篇PRISMA综","AI和GT是智慧农业核心，机器学习、遥感和GEE应用增长；可持续实践以环境监测为主，亚洲和北美领先。","拉丁美洲和非洲等地区基础设施和数据获取受限，可研究低成本、可扩展的AI+遥感解决方案以促进全球农业可持续转型。","2026-09-09T23:30:08.292358Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":434,"tags":436,"primary_topic":440,"secondary_topics":441,"classification":447,"search_phrases":479,"slug":482,"view_count":316,"doi":483,"paper":484,"created_at":501},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",81,{"impact":135,"substance":432,"depth":17,"authority":136,"freshness":200,"relevant":22,"comment":433},20,"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。",[435],{"name":428,"url":425},[27,28,437,438,439],"农业大模型","数字孪生","气候韧性农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[442,443],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":97,"slug":444,"name":445,"path":446,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":46,"locked":14,"version":47,"method":48,"evidence":448},{"candidates":449,"agricultural_evidence":52},[450,455,460,464,469,473],{"code":33,"score":451,"supported":52,"title":452,"abstract":453,"tags":454},19,[54],[56,57,58,59,54],[28,437],{"code":67,"score":456,"supported":52,"title":457,"abstract":458,"tags":459},11,[],[71,438,72],[438,27],{"code":40,"score":13,"supported":52,"title":461,"abstract":462,"tags":463},[],[30,219,63],[439],{"code":97,"score":21,"supported":52,"title":465,"abstract":466,"tags":468},[],[100,101,467],"IoT",[],{"code":75,"score":83,"supported":52,"title":470,"abstract":471,"tags":472},[],[78,79],[],{"code":474,"score":390,"supported":52,"title":475,"abstract":476,"tags":478},"A7",[],[477],"间作",[],[480,481],"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":483,"openalex_id":485,"authors":486,"venue":428,"cited_by_count":113,"oa_url":425,"card":496,"direction":120,"ingested_from":121},"W7221321372",[487,490,493],{"name":488,"orcid":489},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":491,"orcid":492},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":494,"orcid":495},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":497,"method":498,"finding":499,"direction":187,"opportunity":500},"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。","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":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":9,"source_name":508,"source_url":505,"published_at":509,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":510,"sources":512,"tags":514,"primary_topic":516,"secondary_topics":520,"classification":523,"search_phrases":564,"slug":567,"view_count":316,"doi":568,"paper":569,"created_at":587},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",{"impact":17,"substance":135,"depth":451,"authority":275,"freshness":200,"relevant":22,"comment":511},"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。",[513],{"name":508,"url":505},[27,305,57,31,515],"农业干旱",{"code":75,"slug":517,"name":518,"path":519,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[521,522],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":524},{"candidates":525,"agricultural_evidence":52},[526,530,534,538,542,549,555,560],{"code":75,"score":19,"supported":52,"title":527,"abstract":528,"tags":529},[79],[78,79],[31],{"code":40,"score":275,"supported":52,"title":531,"abstract":532,"tags":533},[289],[291,289],[],{"code":33,"score":13,"supported":52,"title":535,"abstract":536,"tags":537},[],[56,57,58,59,54],[57],{"code":302,"score":68,"supported":52,"title":539,"abstract":540,"tags":541},[],[305,306],[305],{"code":543,"score":83,"supported":52,"title":544,"abstract":545,"tags":548},"H2",[],[546,547],"土壤","soil",[],{"code":550,"score":390,"supported":52,"title":551,"abstract":552,"tags":554},"D",[],[553],"水产",[],{"code":90,"score":390,"supported":52,"title":556,"abstract":557,"tags":559},[],[558],"农业生态",[],{"code":67,"score":22,"supported":14,"title":561,"abstract":562,"tags":563},[],[],[27],[565,566],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":568,"openalex_id":570,"authors":571,"venue":508,"cited_by_count":113,"oa_url":581,"card":582,"direction":120,"ingested_from":121},"W7221081979",[572,575,578],{"name":573,"orcid":574},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":576,"orcid":577},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":579,"orcid":580},"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":583,"method":584,"finding":585,"direction":332,"opportunity":586},"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"]