[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3111":3,"related-3111":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":76,"slug":79,"view_count":80,"doi":8,"paper":8,"created_at":81},3111,"江苏省农科院院长朱艳出席 2026 世界农业科技创新大会作\"建设气候韧性农业：中国江苏的经验与策略\"主旨演讲","https:\u002F\u002Fhome.jaas.ac.cn\u002Fznbmxwlsml\u002Fgjhzc\u002Fart\u002F2026\u002Fart_a07fcd60380d494c9ac02c215708d7ea.html","9-15 至 19 2026 年世界农业科技创新大会（WAFI）在北京举行，江苏农科院院长朱艳应邀出席世界农业科学家论坛作主旨演讲。朱艳以\"建设气候韧性农业：中国江苏的经验与策略\"为题分享\"前沿育种—良法配套—智慧赋能\"路径：精准培育气候适应型品种重点创制耐高温耐盐碱节水型优质耐逆种质；精细化开展农艺管理集成示范\"麦—稻周年大面积丰产增效技术体系\"；AI 融入全环节智能管控，构建作物系统模拟模型研发粮食作物逆境灾害动态预警与定量评估技术；分享与 CGIAR、AgMIP 等海外科教机构和国际组织联合开展应对气候变化国际合作进展。",null,"9月15日至19日，2026年世界农业科技创新大会（WAFI）在北京举行。大会以“农食系统绿色健康转型”为主题，由中国农业大学、平谷区人民政府、北京市农业农村局和国际农业研究磋商组织（CGIAR）共同主办。会议期间，院长、党委副书记朱艳应邀出席世界农业科学家论坛并作主旨演讲。\n\n[![Image 1: A3017F2F331F7F14DEFC59D29D081BB5.jpg](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002FSa52f10e390324c7b9184546c22090621-800.jpg)](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002Fa52f10e390324c7b9184546c22090621.jpg)\n\n朱艳以“建设气候韧性农业：中国江苏的经验与策略”为题重点分享了我省在应对气候变化挑战探索出的“前沿育种—良法配套—智慧赋能”路径。一是精准培育气候适应型品种，重点创制耐高温、耐盐碱、节水型优质耐逆种质，育成了一批高产抗逆的水稻、小麦及大豆等新品种，并加快建设江苏省农业种质资源综合基因库，筑牢应对气候变化的“种质”防线。二是精细化开展农艺管理，集成示范“麦—稻周年大面积丰产增效技术体系”，推广水稻集中育秧、小麦适期晚播等避灾技术，同时落实轮作休耕等绿色制度，打好可持续的气候适应“组合拳”。三是AI融入全环节智能管控，构建作物系统模拟模型，研发粮食作物逆境灾害动态预警与定量评估技术，覆盖稻麦全链条生产管理，形成应对气候变化的智慧体系。她还分享了我院与国际农业研究磋商组织（CGIAR）、国际农业模型比较与改进项目（AgMIP）等海外科教机构和国际组织联合开展应对气候变化的国际合作进展。\n\n朱艳表示，江苏省农业科学院愿与全球农业科学家一道，推动建立应对气候变化的全球知识共享平台，贡献更多可复制、可推广的“江苏方案”。\n\n[![Image 2: 朱院-世界农业科技创新大会2.jpg](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002FS4a20548ff6c34efe97426fb6933e1a41-800.jpg)](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002F4a20548ff6c34efe97426fb6933e1a41.jpg)\n\n世界农业科技创新大会（WAFI）由北京市委市政府发起，以“创新农业 共享未来”为宗旨，聚焦促进全球农食系统转型的前沿创新，深入推动产学研互动和国际交流合作。自2023年启动以来，大会已成功举办4届，联合国粮农组织、世界粮食计划署等国际机构深度参与，赢得国内外同仁的高度认可，已成为全球农业科技领域具有实质影响力的高端对话平台之一。","江苏省农业科学院","2026-09-18T06:56:00Z","报道",25,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,18,15,13,6,1,"省级农科院院长在国际大会分享气候韧性农业的江苏路径，内容具体、有国际合作信息，但属会议报道，时效略过，可作主题聚合素材而非头条精选。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"江苏省农业科学院 2026年09月","https:\u002F\u002Fjaas.ac.cn\u002Fznbmxwlsml\u002Fgjhzc\u002Fart\u002F2026\u002Fart_a07fcd60380d494c9ac02c215708d7ea.html",[30,31,32,33,34],"智慧农业","农业人工智能","种业振兴","国际合作","气候韧性农业",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,56,61,66,71],{"code":36,"score":49,"supported":50,"title":51,"abstract":52,"tags":55},7,true,[],[53,54],"气候变化","气候适应",[34],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"F1",[],[],[32],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[30],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I2",[],[],[31],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"J3",[],[],[33],[77,78],"江苏省农科院 气候韧性农业","朱艳 WAFI 主旨演讲","江苏省农科院气候韧性农业-3111",4,"2026-09-22T00:05:36.089846Z",{"total":21,"page":22,"page_size":21,"items":83},[84,191,266,327,410,467],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":94,"score_detail":95,"sources":99,"tags":101,"primary_topic":104,"secondary_topics":109,"classification":116,"search_phrases":163,"slug":166,"view_count":167,"doi":168,"paper":169,"created_at":190},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","论文",10,81,{"impact":17,"substance":96,"depth":18,"authority":20,"freshness":97,"relevant":22,"comment":98},20,8,"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。",[100],{"name":90,"url":87},[30,31,102,103,34],"农业大模型","数字孪生",{"code":67,"slug":105,"name":106,"path":107,"parent_code":108},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[110,111],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":112,"slug":113,"name":114,"path":115,"parent_code":108},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":43,"locked":14,"version":44,"method":45,"evidence":117},{"candidates":118,"agricultural_evidence":50},[119,129,136,141,149,156],{"code":67,"score":120,"supported":50,"title":121,"abstract":123,"tags":128},19,[122],"artificial intelligence",[124,125,126,127,122],"深度学习","机器学习","machine learning","deep learning",[31,102],{"code":62,"score":130,"supported":50,"title":131,"abstract":132,"tags":135},11,[],[133,103,134],"精准农业","precision agriculture",[103,30],{"code":36,"score":93,"supported":50,"title":137,"abstract":138,"tags":140},[],[53,54,139],"climate change",[34],{"code":112,"score":142,"supported":50,"title":143,"abstract":144,"tags":148},9,[],[145,146,147],"物联网","internet of things","IoT",[],{"code":150,"score":21,"supported":50,"title":151,"abstract":152,"tags":155},"I3",[],[153,154],"遥感","remote sensing",[],{"code":157,"score":158,"supported":50,"title":159,"abstract":160,"tags":162},"A7",3,[],[161],"间作",[],[164,165],"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",2,"10.54963\u002Fia.v2i2.103039",{"doi":168,"openalex_id":170,"authors":171,"venue":90,"cited_by_count":181,"oa_url":87,"card":182,"direction":188,"ingested_from":189},"W7221321372",[172,175,178],{"name":173,"orcid":174},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":176,"orcid":177},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":179,"orcid":180},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",0,{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-11T23:30:07.465569Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":8,"source_name":197,"source_url":194,"published_at":198,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":199,"score_detail":200,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":210,"classification":212,"search_phrases":249,"slug":252,"view_count":22,"doi":253,"paper":254,"created_at":265},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":18,"substance":201,"depth":19,"authority":202,"freshness":142,"relevant":22,"comment":203},16,12,"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。",[205],{"name":197,"url":194},[30,31,207,53,208],"可持续发展","遥感监测",{"code":67,"slug":105,"name":106,"path":107,"parent_code":108},[211],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":213},{"candidates":214,"agricultural_evidence":50},[215,219,223,227,231,238,245],{"code":67,"score":18,"supported":50,"title":216,"abstract":217,"tags":218},[122],[124,125,126,127,122],[31],{"code":36,"score":19,"supported":50,"title":220,"abstract":221,"tags":222},[139],[53,139],[53],{"code":62,"score":49,"supported":50,"title":224,"abstract":225,"tags":226},[],[133,134],[30],{"code":150,"score":49,"supported":50,"title":228,"abstract":229,"tags":230},[],[153,154],[208],{"code":232,"score":21,"supported":50,"title":233,"abstract":234,"tags":237},"H4",[],[235,236],"碳排放","carbon emission",[],{"code":239,"score":21,"supported":50,"title":240,"abstract":241,"tags":244},"H6",[],[242,243],"生物多样性","biodiversity",[],{"code":112,"score":21,"supported":50,"title":246,"abstract":247,"tags":248},[],[145,146],[],[250,251],"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":253,"openalex_id":255,"authors":256,"venue":197,"cited_by_count":181,"oa_url":194,"card":259,"direction":188,"ingested_from":189},"W7222092855",[257],{"name":258,"orcid":8},"Deepmala Suryavanshi",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"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":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":8,"source_name":272,"source_url":269,"published_at":273,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":274,"score_detail":275,"sources":278,"tags":280,"primary_topic":284,"secondary_topics":285,"classification":286,"search_phrases":308,"slug":311,"view_count":167,"doi":312,"paper":313,"created_at":326},4342,"GREEN FINANCE AND CAPITAL CIRCULATION IN SMART AGRICULTURE: AI-DRIVEN INSIGHTS","https:\u002F\u002Fdoi.org\u002F10.24412\u002F2411-0450-2026-7-114-120","This scientific article examines the role of digitalization and smart agriculture in advancing green finance, AI-driven innovation, and inclusive rural investment. The main objective of the study is to assess how integrating digital technologies and AI systems into agricultural finance can enhance productivity and support sustainable community development. Drawing on the theoretical foundations of digital finance, green finance, and development policy, the research explores the relationship between traditional credit systems and innovative technological solutions to drive structural transformations in rural economies. The analysis shows that conventional agricultural finance mechanisms are often constrained by limited access to funds, high transaction costs, and insufficient risk management. Digital tools, including mobile banking, fintech platforms, and AI-based credit scoring, improve financial accessibility, optimize investment decisions, and increase transparency, while smart agricultural technologies enhance efficiency, productivity, and climate resilience. The study also discusses long-term trends in global agro-food trade and regional structural characteristics reflecting uneven import and export flows across regions. Overall, the research demonstrates that combining digitalization, AI, and targeted finance can significantly foster more sustainable and productive agricultural development, increasing both efficiency and financial inclusion while supporting long-term resilience in rural communities.","本文考察了数字化与智慧农业在推动绿色金融、人工智能驱动的创新以及包容性农村投资中的作用。研究的主要目标是评估将数字技术和人工智能系统融入农业金融，如何能够提升生产力并支持可持续的社区发展。基于数字金融、绿色金融和发展政策的理论基础，研究探讨了传统信贷体系与创新技术解决方案之间的关系，以推动农村经济的结构性转型。分析表明，传统农业金融机制往往受限于资金获取渠道有限、交易成本高以及风险管理不足。数字工具，包括移动银行、金融科技平台和基于人工智能的信用评分，改善了金融可及性，优化了投资决策，并提高了透明度，而智慧农业技术则提升了效率、生产力和气候韧性。研究还讨论了全球农食产品贸易的长期趋势以及反映各区域进出口流量不均衡的区域结构特征。总体而言，研究表明，将数字化、人工智能和有针对性的金融相结合，能够显著促进更可持续、更具生产力的农业发展，在提高效率和金融包容性的同时，支持农村社区的长期韧性。","cyberleninka.ru","2026-10-06T00:00:00Z",72,{"impact":18,"substance":96,"depth":276,"authority":97,"freshness":142,"relevant":22,"comment":277},17,"论文从数字金融与AI信贷视角切入智慧农业与绿色金融，结论具参考价值，但来源为俄语开放期刊，权威性一般，适合作为主题聚合素材而非头条精选。",[279],{"name":272,"url":269},[30,31,281,282,283],"数字普惠金融","农村金融","绿色金融",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":287},{"candidates":288,"agricultural_evidence":50},[289,294,299,304],{"code":62,"score":19,"supported":50,"title":290,"abstract":292,"tags":293},[291],"smart agriculture",[30,291],[30],{"code":295,"score":158,"supported":14,"title":296,"abstract":297,"tags":298},"G3",[],[],[282,281,283],{"code":36,"score":158,"supported":50,"title":300,"abstract":301,"tags":303},[],[302],"climate resilience",[],{"code":67,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[31],[309,310],"智慧农业 绿色金融","AI 农业信贷 数字金融","智慧农业绿色金融-4342","10.24412\u002F2411-0450-2026-7-114-120",{"doi":312,"openalex_id":314,"authors":315,"venue":272,"cited_by_count":181,"oa_url":269,"card":320,"direction":188,"ingested_from":189},"W7220700804",[316,318],{"name":317,"orcid":8},"A.V. Mardoyan",{"name":319,"orcid":8},"H.A. Sargsyan",{"tldr":321,"method":322,"finding":323,"direction":324,"opportunity":325},"研究数字化与智慧农业如何推动绿色金融、AI创新与农村包容性投资。","基于数字金融、绿色金融与发展政策理论，分析移动银行、金融科技与AI信用评分。","数字工具与AI可提升金融可及性、优化投资并增强农业气候韧性。","数字乡村与农业信息化","可实证检验AI信用评分对农户信贷可得性与绿色投资决策的因果影响。","2026-10-07T23:30:08.959053Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":8,"source_name":333,"source_url":330,"published_at":334,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":94,"score_detail":335,"sources":338,"tags":340,"primary_topic":342,"secondary_topics":343,"classification":346,"search_phrases":391,"slug":394,"view_count":167,"doi":395,"paper":396,"created_at":409},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":18,"substance":17,"depth":18,"authority":336,"freshness":142,"relevant":22,"comment":337},14,"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。",[339],{"name":333,"url":330},[30,31,145,103,341],"作物产量预测",{"code":67,"slug":105,"name":106,"path":107,"parent_code":108},[344,345],{"code":112,"slug":113,"name":114,"path":115,"parent_code":108},{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":347},{"candidates":348,"agricultural_evidence":50},[349,354,359,363,367,374,381,387],{"code":62,"score":120,"supported":50,"title":350,"abstract":352,"tags":353},[351],"digital twin",[30,103,291,351],[103,30],{"code":67,"score":202,"supported":50,"title":355,"abstract":357,"tags":358},[356],"decision support",[122],[31],{"code":112,"score":93,"supported":50,"title":360,"abstract":361,"tags":362},[],[145,146,147],[145],{"code":36,"score":142,"supported":50,"title":364,"abstract":365,"tags":366},[],[53,54,139],[],{"code":368,"score":21,"supported":50,"title":369,"abstract":370,"tags":373},"H2",[],[371,372],"土壤","soil",[],{"code":375,"score":21,"supported":50,"title":376,"abstract":377,"tags":380},"H3",[],[378,379],"灌溉","irrigation",[],{"code":382,"score":158,"supported":50,"title":383,"abstract":384,"tags":386},"H8",[],[385],"可持续农业",[],{"code":150,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[341],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":333,"cited_by_count":181,"oa_url":330,"card":404,"direction":188,"ingested_from":189},"W7219849006",[399,402],{"name":400,"orcid":401},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":403,"orcid":8},"Munish Bhatia",{"tldr":405,"method":406,"finding":407,"direction":188,"opportunity":408},"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",{"id":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":8,"source_name":416,"source_url":413,"published_at":417,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":418,"score_detail":419,"sources":421,"tags":423,"primary_topic":424,"secondary_topics":425,"classification":428,"search_phrases":449,"slug":452,"view_count":167,"doi":453,"paper":454,"created_at":466},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":18,"substance":96,"depth":18,"authority":20,"freshness":142,"relevant":22,"comment":420},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[422],{"name":416,"url":413},[30,31,145,103,133],{"code":112,"slug":113,"name":114,"path":115,"parent_code":108},[426,427],{"code":67,"slug":105,"name":106,"path":107,"parent_code":108},{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":429},{"candidates":430,"agricultural_evidence":50},[431,437,441,445],{"code":112,"score":18,"supported":50,"title":432,"abstract":433,"tags":436},[146],[145,434,146,147,435],"边缘计算","edge computing",[145],{"code":62,"score":202,"supported":50,"title":438,"abstract":439,"tags":440},[],[133,103,134],[103,30,133],{"code":67,"score":202,"supported":50,"title":442,"abstract":443,"tags":444},[122],[122],[31],{"code":36,"score":142,"supported":50,"title":446,"abstract":447,"tags":448},[],[53,139,302],[],[450,451],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":453,"openalex_id":455,"authors":456,"venue":416,"cited_by_count":181,"oa_url":413,"card":460,"direction":188,"ingested_from":189},"W7216004668",[457],{"name":458,"orcid":459},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":461,"method":462,"finding":463,"direction":464,"opportunity":465},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":8,"source_name":473,"source_url":470,"published_at":474,"category":92,"cover_url":8,"hotness":93,"is_selected":14,"score":475,"score_detail":476,"sources":478,"tags":480,"primary_topic":483,"secondary_topics":488,"classification":494,"search_phrases":533,"slug":536,"view_count":537,"doi":538,"paper":539,"created_at":562},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":202,"substance":18,"depth":201,"authority":202,"freshness":97,"relevant":22,"comment":477},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[479],{"name":473,"url":470},[30,31,153,481,482],"GIS","干旱监测",{"code":484,"slug":485,"name":486,"path":487,"parent_code":108},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[489,490],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":150,"slug":491,"name":492,"path":493,"parent_code":108},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":43,"locked":14,"version":44,"method":45,"evidence":495},{"candidates":496,"agricultural_evidence":50},[497,505,511,515,519,523,529],{"code":484,"score":18,"supported":50,"title":498,"abstract":499,"tags":504},[481],[500,501,481,502,503],"地理信息","空间分析","spatial analysis","geographic information",[481],{"code":36,"score":19,"supported":50,"title":506,"abstract":508,"tags":510},[507],"drought",[509,507],"干旱",[482],{"code":150,"score":19,"supported":50,"title":512,"abstract":513,"tags":514},[154],[153,154],[153],{"code":67,"score":93,"supported":50,"title":516,"abstract":517,"tags":518},[],[124,125,126,127,122],[31],{"code":368,"score":21,"supported":50,"title":520,"abstract":521,"tags":522},[],[371,372],[],{"code":375,"score":21,"supported":50,"title":524,"abstract":525,"tags":528},[],[526,527],"水资源","evapotranspiration",[],{"code":62,"score":22,"supported":14,"title":530,"abstract":531,"tags":532},[],[],[30],[534,535],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":538,"openalex_id":540,"authors":541,"venue":473,"cited_by_count":181,"oa_url":470,"card":556,"direction":560,"ingested_from":189},"W7214156857",[542,544,546,548,550,552,554],{"name":543,"orcid":8},"V. Dhanalakshmi",{"name":545,"orcid":8},"N. Manikandan",{"name":547,"orcid":8},"V. S. Jinsy",{"name":549,"orcid":8},"K. V. Sumesh",{"name":551,"orcid":8},"P. Nideesh",{"name":553,"orcid":8},"P. S. Manju",{"name":555,"orcid":8},"N. Gopika",{"tldr":557,"method":558,"finding":559,"direction":560,"opportunity":561},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","农业遥感与作物表型","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z"]