[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3353":3,"related-3353":56},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":55},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。",null,"Sustainable Futures","2026-09-23T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","可持续发展","气候变化","文献计量",[32,33],"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",0,"10.1016\u002Fj.sftr.2026.102145",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":47,"direction":53,"ingested_from":54},"W7214099405",[40,43,45],{"name":41,"orcid":42},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":44,"orcid":9},"Shiyan Zhai",{"name":46,"orcid":9},"Yuke Feng",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"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。","智慧农业 \u002F 农业物联网","openalex","2026-09-24T23:30:10.248845Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,111,151,187,228,259],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":74,"tags":76,"search_phrases":80,"slug":83,"view_count":35,"doi":84,"paper":85,"created_at":110},2775,"Digital-enabled life cycle thinking for sustainable food systems: aligning with sustainable development goals","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-63273-w","The increasing complexity of global food systems, coupled with climate change, resource scarcity, and socio-economic pressures, necessitates advanced sustainability assessment approaches. While life cycle thinking (LCT) provides a comprehensive framework, its practical application is constrained by data limitations, methodological uncertainty, and the limited ability of conventional models to capture dynamic operational conditions. This study proposes a digitally integrated LCT framework that combines artificial intelligence (AI), blockchain, internet of things (IoT), and remote sensing to enhance data accuracy, transparency, and real-time decision-making in food supply chains. A key contribution of this research is the introduction of two novel constructs, the digital trust index (DTI) and proof-of-climate impact (PCI) which quantify stakeholder trust and provide verifiable evidence linking digital interventions to measurable environmental outcomes. The DTI is conceptualized as a composite measure of stakeholder trust, integrating both digital infrastructure capabilities and governance-based trust mechanisms. Scenario analysis demonstrates that integrated digital and governance systems can achieve up to 34% climate impact reduction (CIR), while Monte Carlo simulation results indicate an average CIR of approximately 20%, confirming the robustness of the framework under uncertainty. The study reveals that SDG 12 (23%) and SDG 9 (20%) dominate digital LCT research in food systems, followed by SDG 2 and SDG 13, while biodiversity-focused goals (SDG 14 & SDG 15) receive less attention. The proposed SDG oriented framework integrates digital technologies across LCT phases, enabling real-time and transparent sustainability assessment while enhancing the monitoring of biodiversity impacts. The findings highlight that digitalization, when coupled with governance and sustainable development goal-oriented metrics, enables more transparent, adaptive, and impact-driven sustainability assessment in food systems.","全球粮食系统日益复杂，加之气候变化、资源稀缺和社会经济压力，迫切需要先进的可持续性评估方法。尽管生命周期思维（LCT）提供了全面的框架，但其实际应用受到数据限制、方法学不确定性以及传统模型捕捉动态运行条件能力有限的制约。本研究提出了一种数字化集成的LCT框架，将人工智能（AI）、区块链、物联网（IoT）和遥感相结合，以提升食品供应链中的数据准确性、透明度和实时决策能力。本研究的一项关键贡献是引入了两个新颖构念——数字信任指数（DTI）和气候影响证明（PCI），用以量化利益相关者信任，并提供可验证的证据，将数字干预与可衡量的环境成果相联系。DTI被概念化为利益相关者信任的综合度量，整合了数字基础设施能力和基于治理的信任机制。情景分析表明，集成的数字与治理系统可实现高达34%的气候影响减排（CIR），而蒙特卡洛模拟结果显示平均CIR约为20%，证实了该框架在不确定性下的稳健性。研究揭示，SDG 12（23%）和SDG 9（20%）在食品系统中的数字LCT研究中占主导地位，其次是SDG 2和SDG 13，而聚焦生物多样性的目标（SDG 14和SDG 15）受到的关注较少。所提出的面向SDG的框架将数字技术整合到LCT各阶段，实现实时、透明的可持续性评估，同时增强对生物多样性影响的监测。研究结果表明，数字化在与治理和面向可持续发展目标的指标相结合时，能够在食品系统中实现更透明、更具适应性和以影响为导向的可持续性评估。","Scientific Reports","2026-09-15T00:00:00Z",86,{"impact":18,"substance":69,"depth":70,"authority":71,"freshness":72,"relevant":21,"comment":73},23,19,14,8,"提出数字信任指数与气候影响证明等新构念，量化数字技术对食物系统可持续性的减排贡献，方法新颖、结论可靠，对农业信息化与绿色转型具有较强参考价值。",[75],{"name":65,"url":62},[26,27,77,28,78,79],"农业遥感","区块链溯源","食物系统",[81,82],"农业人工智能 区块链溯源 可持续发展 农业遥感","农业人工智能 区块链溯源","农业人工智能区块链溯源可持续发展农业遥感-2775","10.1038\u002Fs41598-026-63273-w",{"doi":84,"openalex_id":86,"authors":87,"venue":65,"cited_by_count":35,"oa_url":103,"card":104,"direction":53,"ingested_from":54},"W7213239181",[88,90,93,95,97,100],{"name":89,"orcid":9},"S. U. Parvathy",{"name":91,"orcid":92},"Vysakh Kani Kolil","https:\u002F\u002Forcid.org\u002F0000-0003-2035-3439",{"name":94,"orcid":9},"Raghu Raman",{"name":96,"orcid":9},"Nripendra P. Rana",{"name":98,"orcid":99},"Sasangan Ramanathan","https:\u002F\u002Forcid.org\u002F0000-0002-7459-0934",{"name":101,"orcid":102},"Krishnashree Achuthan","https:\u002F\u002Forcid.org\u002F0000-0003-2618-0882","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63273-w_reference.pdf",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出融合AI、区块链、物联网与遥感的数字生命周期思维框架，用于食品系统可持续性评估。","整合AI、区块链、IoT与遥感，构建数字信任指数和气候影响证明，用情景分析与蒙特","数字与治理结合可使气候影响降低最高34%，平均约20%；SDG12和SDG9主导相关研究。","农业绿色发展与碳","可探索数字信任指数与气候影响证明在农业食品供应链中的实证验证及生物多样性目标监测。","2026-09-17T23:30:15.308110Z",{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":123,"tags":125,"search_phrases":128,"slug":130,"view_count":35,"doi":131,"paper":132,"created_at":150},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)",72,{"impact":17,"substance":17,"depth":120,"authority":121,"freshness":72,"relevant":21,"comment":122},16,12,"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。",[124],{"name":117,"url":114},[26,27,28,126,127],"精准农业","环境韧性",[129,33],"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":131,"openalex_id":133,"authors":134,"venue":117,"cited_by_count":35,"oa_url":114,"card":145,"direction":53,"ingested_from":54},"W7213274173",[135,137,140,143],{"name":136,"orcid":9},"Sri Poedji Lestari",{"name":138,"orcid":139},"Rosa Lesmana","https:\u002F\u002Forcid.org\u002F0009-0002-5308-6023",{"name":141,"orcid":142},"Sabil Maulana Fauzi","https:\u002F\u002Forcid.org\u002F0009-0003-1833-1027",{"name":144,"orcid":9},"Alexander Johnson Johnson",{"tldr":146,"method":147,"finding":148,"direction":108,"opportunity":149},"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":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"search_phrases":170,"slug":173,"view_count":35,"doi":174,"paper":175,"created_at":186},2040,"Climate Change and Agricultural Insect Pests: Ecological Mechanisms, Crop Productivity Impacts and Climate Adaptation Strategies","https:\u002F\u002Fdoi.org\u002F10.47495\u002Fokufbed.2000761","Climate change has emerged as one of the most significant challenges affecting agricultural production systems worldwide. Rising temperatures, altered precipitation patterns, increasing atmospheric carbon dioxide concentrations, and the growing frequency of extreme weather events are substantially influencing the biology, ecology, distribution, and population dynamics of agricultural pests. These changes contribute to increased pest abundance, expanded geographical ranges, higher overwintering success, accelerated development rates, and greater numbers of generations per year. Consequently, pest-induced crop losses are expected to increase, posing serious threats to crop productivity, agricultural sustainability, and global food security. In addition to direct effects on pest populations, climate change disrupts plant–pest–natural enemy interactions and weakens biological control mechanisms, further increasing pest pressure within agricultural ecosystems. This review examines the effects of climate change on agricultural insect pest populations and evaluates their implications for crop productivity. Particular attention is given to temperature increases, changes in precipitation and humidity regimes, geographical distribution shifts, invasive species expansion, and phenological mismatches. Furthermore, innovative adaptation strategies including integrated pest management, artificial intelligence-based forecasting systems, precision agriculture technologies, climate-smart agriculture approaches, and remote sensing applications are discussed as potential tools for enhancing agricultural resilience under changing climatic conditions. The findings indicate that sustainable management of climate-related pest risks requires multidisciplinary approaches integrating climate information, pest monitoring, ecological processes, and advanced decision-support technologies. Developing climate-resilient, technology-supported and ecologically based pest management strategies will be essential for safeguarding agricultural productivity and long-term global food security.","气候变化已成为影响全球农业生产系统的最重大挑战之一。气温上升、降水模式改变、大气二氧化碳浓度增加以及极端天气事件日益频繁，正在显著影响农业害虫的生物学、生态学、分布和种群动态。这些变化导致害虫丰度增加、地理分布范围扩大、越冬成功率提高、发育速率加快以及每年世代数增多。因此，害虫引起的作物损失预计将增加，对作物生产力、农业可持续性和全球粮食安全构成严重威胁。除对害虫种群的直接影响外，气候变化还扰乱了植物—害虫—天敌之间的相互作用，削弱了生物防治机制，进一步加剧了农业生态系统内的害虫压力。本文综述了气候变化对农业害虫种群的影响，并评估了其对作物生产力的意义。特别关注了温度升高、降水和湿度状况变化、地理分布转移、入侵物种扩散以及物候错配等方面。此外，还讨论了创新性适应策略，包括有害生物综合治理、基于人工智能的预测系统、精准农业技术、气候智慧型农业方法以及遥感应用，作为在气候变化条件下增强农业韧性的潜在工具。研究结果表明，气候相关害虫风险的可持续管理需要多学科方法，整合气候信息、害虫监测、生态过程和先进决策支持技术。开发气候韧性、技术支撑和基于生态的害虫管理策略，对于保障农业生产力和长期全球粮食安全至关重要。","Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi","2026-09-09T00:00:00Z",76,{"impact":17,"substance":161,"depth":162,"authority":121,"freshness":20,"relevant":21,"comment":163},20,17,"系统综述气候变化对农业害虫生态机制与作物生产力的影响，并整合IPM、AI预测、精准农业与遥感等适应策略，对智慧植保与气候韧性农业有参考价值。",[165],{"name":157,"url":154},[26,27,167,168,29,169],"粮食安全","病虫害防控","遥感监测",[171,172],"农业人工智能 病虫害防控 智慧农业 气候变化","农业人工智能 病虫害防控","农业人工智能病虫害防控智慧农业气候变化-2040","10.47495\u002Fokufbed.2000761",{"doi":174,"openalex_id":176,"authors":177,"venue":157,"cited_by_count":35,"oa_url":154,"card":181,"direction":53,"ingested_from":54},"W7212022123",[178],{"name":179,"orcid":180},"Ekrem ASLAN","https:\u002F\u002Forcid.org\u002F0000-0001-8829-7301",{"tldr":182,"method":183,"finding":184,"direction":51,"opportunity":185},"综述气候变化对农业害虫生态机制、作物生产力影响及气候适应策略。","文献综述，整合气候数据、害虫监测与AI预测、遥感等技术。","气候变暖扩大害虫分布、增加世代与危害，削弱生物防治，威胁粮食安全。","可构建融合气候、遥感与AI的害虫风险预警决策模型，填补多尺度动态预测空白。","2026-09-10T23:30:09.295781Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":197,"tags":199,"search_phrases":201,"slug":203,"view_count":35,"doi":204,"paper":205,"created_at":227},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":18,"depth":17,"authority":121,"freshness":21,"relevant":21,"comment":196},"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。",[198],{"name":193,"url":190},[26,27,28,200],"遥感技术",[202,33],"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":204,"openalex_id":206,"authors":207,"venue":193,"cited_by_count":35,"oa_url":190,"card":222,"direction":53,"ingested_from":54},"W7211954581",[208,210,212,214,216,219],{"name":209,"orcid":9},"Paulo César Escandón Panchana",{"name":211,"orcid":9},"Aline Maria Meiguins de Lima",{"name":213,"orcid":9},"Andrés Velastegui-Montoya",{"name":215,"orcid":9},"Eddy Sanclemente Ordoñez",{"name":217,"orcid":218},"Fernanda Calderón","https:\u002F\u002Forcid.org\u002F0000-0002-5191-7527",{"name":220,"orcid":221},"Sandra Martínez Cuevas","https:\u002F\u002Forcid.org\u002F0000-0002-2150-3251",{"tldr":223,"method":224,"finding":225,"direction":51,"opportunity":226},"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":229,"title":230,"url":231,"summary":232,"summary_zh":9,"content":9,"source_name":233,"source_url":231,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":239,"tags":241,"search_phrases":242,"slug":244,"view_count":35,"doi":245,"paper":246,"created_at":258},1270,"Tendencias en agricultura y silvicultura de precisión: un análisis bibliométrico de la integración de datos, el apoyo a la toma de decisiones y la sostenibilidad","https:\u002F\u002Fdoi.org\u002F10.69639\u002Farandu.v13i3.2452","Este estudio analiza las tendencias y la estructura conceptual de la investigación sobre agricultura y silvicultura de precisión, con énfasis en la integración de datos, los sistemas de apoyo a la toma de decisiones y la sostenibilidad. Se desarrolló un análisis bibliométrico de documentos indexados en Web of Science y Scopus, recuperados mediante una estrategia de búsqueda orientada a identificar estudios relacionados con tecnologías digitales, inteligencia artificial, teledetección, IoT, modelización predictiva y gestión sostenible. El proceso de selección documental siguió criterios de depuración basados en PRISMA, clasificación por cuartiles y revisión de metadatos, obteniéndose un corpus final de 411 documentos publicados entre 2003 y 2026. Los resultados evidencian un crecimiento acelerado de la producción científica, con una alta concentración de literatura reciente y una estructura temática organizada en torno a temas motores, básicos, de nicho y emergentes. Los clústeres de precision agriculture y smart agriculture se identificaron como núcleos articuladores del campo, mientras que inteligencia artificial, IoT y seguridad alimentaria funcionaron como bases transversales. Asimismo, la agricultura digital, la gobernanza tecnológica, el aprendizaje por refuerzo y las redes neuronales convolucionales aparecen como líneas especializadas o en desarrollo. Se concluye que el mapa temático permite reconocer patrones de centralidad y densidad que diferencian tendencias consolidadas, especializadas y emergentes, aportando una visión ordenada para orientar futuras investigaciones sobre sistemas agroforestales inteligentes, adaptativos y sostenibles.","Arandu-UTIC.","2026-08-29T00:00:00Z",69,{"impact":17,"substance":161,"depth":17,"authority":13,"freshness":237,"relevant":21,"comment":238},3,"基于411篇文献的计量分析，揭示精准农业与林业研究趋势，对智慧农业领域有参考价值。",[240],{"name":233,"url":231},[26,27,77,28,126],[243,33],"农业人工智能 可持续发展 农业遥感 智慧农业","农业人工智能可持续发展农业遥感智慧农业-1270","10.69639\u002Farandu.v13i3.2452",{"doi":245,"openalex_id":247,"authors":248,"venue":233,"cited_by_count":35,"oa_url":252,"card":253,"direction":53,"ingested_from":54},"W7204668132",[249],{"name":250,"orcid":251},"Carlos Arturo Carvajal Chávez","https:\u002F\u002Forcid.org\u002F0000-0002-2781-6953","https:\u002F\u002Frevista.utic.edu.py\u002Frevista.ojs\u002Findex.php\u002Frevistas\u002Farticle\u002Fdownload\u002F2452\u002F3991",{"tldr":254,"method":255,"finding":256,"direction":51,"opportunity":257},"通过文献计量分析，梳理精准农业与林业的研究趋势、主题结构和热点方向。","基于Web of Science和Scopus，采用PRISMA筛选，对411篇","精准农业和智慧农业是核心主题，AI、IoT和食品安全为基础，数字农业、强化学习等为新兴方向。","可深入探索强化学习与卷积神经网络在智能农林业系统中的应用，结合数据集成与可持续性，填补新兴技术整合研究的空白。","2026-09-01T04:03:13.088508Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":9,"content":9,"source_name":264,"source_url":262,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"search_phrases":272,"slug":275,"view_count":35,"doi":276,"paper":277,"created_at":289},1267,"Artificial Intelligence for Climate-Smart Agriculture: A Review of Applications in Yield Prediction, Weather Forecasting, Irrigation Management and Pest and Disease Detection","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug707","Climate change is increasingly disrupting agricultural systems worldwide through more frequent extreme weather events, shifting rainfall patterns, and rising temperatures. These changes contribute to reduced crop yields, unstable food supplies, and heightened production risks, especially in vulnerable developing regions. In response, ClimateSmart Agriculture (CSA) has been developed as a strategic framework to improve agricultural productivity while enhancing resilience and promoting environmentally sustainable farming practices. Within this framework, Artificial Intelligence (AI) is increasingly recognized as a transformative tool capable of supporting data-driven agricultural decision-making. This review systematically synthesizes recent literature on AI applications in CSA, focusing on four key thematic areas: crop yield prediction, weather and climate forecasting, irrigation and water management, and pest and disease detection under climate stress. Peer-reviewed articles published between 2020 and 2026 were retrieved from Google Scholar, Semantic Scholar, and Crossref. A total of 170 studies were initially identified, of which 58 highly relevant articles were selected following screening based on relevance, quality, and thematic alignment. Findings indicate that AIbased systems significantly improve predictive accuracy, optimize resource use, and enhance early warning capabilities across agricultural systems. Machine learning, deep learning, remote sensing, IoT, and big data analytics are widely applied to support precision agriculture and climate adaptation strategies. However, key challenges such as data scarcity in developing regions, high implementation costs, limited farmer adoption, and climate uncertainty continue to constrain widespread adoption. The review concludes that AI holds strong potential to transform CSA by improving productivity, resilience, and sustainability. Nonetheless, its effectiveness depends on the development of accessible, affordable, and context-specific solutions supported by strong policy frameworks and improved digital infrastructure.","International Journal of Innovative Science and Research Technology (IJISRT)",67,{"impact":17,"substance":161,"depth":17,"authority":72,"freshness":237,"relevant":21,"comment":267},"系统综述AI在气候智慧农业中的应用，覆盖产量预测、灌溉、病虫害等，信息量大，但来源权威性一般且时效性较低。",[269],{"name":264,"url":262},[26,27,271,126,29],"综述",[273,274],"农业人工智能 智慧农业 气候变化 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业气候变化精准农业-1267","10.38124\u002Fijisrt\u002F26aug707",{"doi":276,"openalex_id":278,"authors":279,"venue":264,"cited_by_count":35,"oa_url":283,"card":284,"direction":53,"ingested_from":54},"W7204643386",[280],{"name":281,"orcid":282},"Sonny Gad Attipoe","https:\u002F\u002Forcid.org\u002F0000-0002-8981-6756","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG707.pdf",{"tldr":285,"method":286,"finding":287,"direction":51,"opportunity":288},"综述AI在气候智慧农业中的应用，涵盖产量预测、天气预报、灌溉管理和病虫害检测。","系统综述2020-2026年文献，筛选58篇相关研究，分析ML、DL、遥感、Io","AI显著提升预测精度和资源利用，但面临数据稀缺、成本高、采纳率低等挑战。","可研究低成本、易用的AI解决方案，结合政策与数字基础设施，促进发展中国家CSA采纳。","2026-09-01T04:03:12.892133Z"]