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年试点；强化农业节水增效，深化农业水价综合改革、推广水肥一体化、浅埋滴灌。",null,"农民日报头版头条","2026-09-03T22:00:00Z","报道",10,true,90,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},28,22,16,15,9,1,"副总理调研强调智慧农业与防灾减灾，政策导向明确，信息权威且时效性强。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","高标准农田","防灾减灾","秋粮生产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":39,"locked":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":13},[45,51,55,63,70,76,82,87,92],{"code":46,"score":47,"supported":13,"title":48,"abstract":49,"tags":50},"I1",12,[26],[26],[26],{"code":32,"score":20,"supported":13,"title":52,"abstract":53,"tags":54},[29],[],[29],{"code":56,"score":57,"supported":13,"title":58,"abstract":59,"tags":62},"G2",6,[],[60,61],"农民合作社","社会化服务",[],{"code":64,"score":65,"supported":13,"title":66,"abstract":67,"tags":69},"F3",3,[],[68],"水肥一体化",[],{"code":71,"score":65,"supported":13,"title":72,"abstract":73,"tags":75},"F5",[],[74],"农机",[],{"code":77,"score":65,"supported":13,"title":78,"abstract":79,"tags":81},"H3",[],[80],"农业节水",[],{"code":83,"score":21,"supported":40,"title":84,"abstract":85,"tags":86},"H1",[],[],[28],{"code":88,"score":21,"supported":40,"title":89,"abstract":90,"tags":91},"I2",[],[],[27],{"code":93,"score":21,"supported":40,"title":94,"abstract":95,"tags":96},"J4",[],[],[30],[98,99],"农业人工智能 高标准农田 智慧农业 秋粮生产","农业人工智能 高标准农田","农业人工智能高标准农田智慧农业秋粮生产-1542",4,"2026-09-04T00:05:29.693313Z",{"total":57,"page":21,"page_size":57,"items":104},[105,151,191,287,370,431],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":8,"content":110,"source_name":111,"source_url":8,"published_at":112,"category":113,"cover_url":8,"hotness":12,"is_selected":40,"score":114,"score_detail":115,"sources":119,"tags":121,"primary_topic":124,"secondary_topics":125,"classification":126,"search_phrases":145,"slug":148,"view_count":149,"doi":8,"paper":8,"created_at":150},1332,"农业农村部党组传达学习习近平总书记重要指示精神，部署秋粮生产和农业防灾减灾救灾","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202608\u002Ft20260828_8867651.htm","8 月 28 日，农业农村部党组召开会议，传达学习习近平总书记关于巩固拓展学习教育成果和对西藏日喀则吉隆县泥石流灾害的重要指示精神。强调要加强秋粮田间管理，抓实农业防灾减灾救灾和病虫害防控，全力夺取秋粮丰收；并研究超强厄尔尼诺对今明两年农业生产的影响。","[![Image 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8月28日，农业农村部党组召开会议，部党组书记、部长张柱主持。\n\n会议传达学习习近平总书记对巩固拓展树立和践行正确政绩观学习教育成果的重要指示精神，强调要深入学习领会习近平总书记重要指示精神，落实中央党的建设工作领导小组会议部署，抓好学习教育后续工作，持续巩固拓展树立和践行正确政绩观学习教育成果，强化政治机关建设，进一步深化问题整改整治，针对性抓好建章立制，纵深推进部系统全面从严治党，办好“三农”领域民生实事，以正确政绩观支撑农业农村高质量发展。\n\n会议学习贯彻习近平总书记对西藏日喀则市吉隆县遭受泥石流灾害的重要指示精神，强调当前正处于防汛关键期，农业防灾减灾形势复杂，要树牢极限思维、底线思维，毫不放松抓好农业防灾减灾救灾，坚决打赢抗灾夺丰收攻坚战。要加密监测预警，强化临灾“叫应”，指导各地落实落细防灾减灾关键措施，尽可能减轻灾害损失。要深入研判超强厄尔尼诺对今明两年农业生产的影响，指导各地制定应急预案、细化防范措施，掌握防灾减灾的主动权。\n\n会议研究部署近期“三农”工作，强调要认真贯彻党中央、国务院部署要求，落实李强总理调研指示精神，抓紧抓实各项重点工作。要加强秋粮田间管理，抓实农业防灾减灾救灾和病虫害防控，全力夺取秋粮丰收。要持续提升农业综合生产能力，着力强化农业科技支撑，系统改进和加强高标准农田建设全链条管理，实施好新一轮黑土地保护工程，夯实粮食安全根基。要发展多种形式的农业适度规模经营，提高新型农业经营主体发展质量，推动农业社会化服务扩面提质，更好服务带动小农户。要积极发展现代种养，大力发展农产品精深加工，因地制宜发展现代设施农业，着力把农业建成现代化大产业。\n\n会议还研究了其他事项。\n\n![Image 3](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_back.png)\n返回顶部\n\n*   [外交部](https:\u002F\u002Fwww.fmprc.gov.cn\u002Fweb\u002F)\n*   [国防部](http:\u002F\u002Fwww.mod.gov.cn\u002F)\n*   [国家发展和改革委员会](https:\u002F\u002Fwww.ndrc.gov.cn\u002F)\n*   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2026-08-28","2026-08-28T00:00:00Z","政策",88,{"impact":16,"substance":17,"depth":116,"authority":19,"freshness":117,"relevant":21,"comment":118},18,5,"农业农村部部署秋粮生产与防灾减灾，政策层级高，信息量大，时效稍逊。",[120],{"name":111,"url":108},[28,29,30,122,123],"农业社会化服务","厄尔尼诺",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":127},{"candidates":128,"agricultural_evidence":13},[129,133,137,141],{"code":32,"score":47,"supported":13,"title":130,"abstract":131,"tags":132},[29],[29],[29],{"code":56,"score":21,"supported":40,"title":134,"abstract":135,"tags":136},[],[],[122],{"code":83,"score":21,"supported":40,"title":138,"abstract":139,"tags":140},[],[],[28],{"code":93,"score":21,"supported":40,"title":142,"abstract":143,"tags":144},[],[],[30],[146,147],"农业社会化服务 高标准农田 厄尔尼诺 秋粮生产","农业社会化服务 高标准农田","农业社会化服务高标准农田厄尔尼诺秋粮生产-1332",2,"2026-09-02T00:05:02.854393Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":8,"content":8,"source_name":156,"source_url":8,"published_at":157,"category":113,"cover_url":8,"hotness":12,"is_selected":40,"score":114,"score_detail":158,"sources":161,"tags":163,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":186,"slug":189,"view_count":101,"doi":8,"paper":8,"created_at":190},798,"扎实做好农业防灾减灾工作 全力以赴夺取全年粮食丰收——刘国中在农业农村部召开视频调度会部署秋粮生产和防灾减灾","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260822\u002F20260822_1\u002Fnmrb_20260822_13384_1_2090908423143133192.html","中共中央政治局委员、国务院副总理刘国中8月21日到农业农村部召开视频调度会,部署秋粮生产和农业防灾减灾。他强调秋粮是全年粮食生产大头、当前正值产量形成关键期,要密切监测雨情汛情墒情苗情病虫情,分区分类推进秋粮田管,抓紧组织\"一喷多促\"作业,关口前移防范后期气象灾害和病虫害,对因灾返贫致贫风险农户及时帮扶。8月20日,刘国中还到河北调研秋粮长势、高标准农田建设、农资供应保障和气象为农服务情况。","农民日报 2026年8月22日 头版头条","2026-08-22T00:00:00Z",{"impact":16,"substance":159,"depth":19,"authority":19,"freshness":12,"relevant":21,"comment":160},20,"国家层面部署秋粮生产与防灾减灾，政策性强，信息权威，时效性高。",[162],{"name":156,"url":154},[28,29,30,164,165,166],"农民日报","刘国中","头版头条",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":170},{"candidates":171,"agricultural_evidence":13},[172,176,182],{"code":32,"score":47,"supported":13,"title":173,"abstract":174,"tags":175},[29],[29],[29],{"code":83,"score":177,"supported":13,"title":178,"abstract":179,"tags":181},7,[],[28,180],"农田建设",[28],{"code":93,"score":21,"supported":40,"title":183,"abstract":184,"tags":185},[],[],[30],[187,188],"高标准农田 农民日报 头版头条 秋粮生产","高标准农田 农民日报","高标准农田农民日报头版头条秋粮生产-798","2026-08-22T00:05:56.533177Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":8,"source_name":197,"source_url":194,"published_at":198,"category":199,"cover_url":8,"hotness":12,"is_selected":40,"score":200,"score_detail":201,"sources":203,"tags":205,"primary_topic":209,"secondary_topics":214,"classification":216,"search_phrases":267,"slug":270,"view_count":21,"doi":271,"paper":272,"created_at":286},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","论文",70,{"impact":116,"substance":18,"depth":19,"authority":47,"freshness":20,"relevant":21,"comment":202},"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。",[204],{"name":197,"url":194},[26,27,206,207,208],"可持续发展","气候变化","遥感监测",{"code":88,"slug":210,"name":211,"path":212,"parent_code":213},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[215],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":217},{"candidates":218,"agricultural_evidence":13},[219,228,233,239,246,253,260],{"code":88,"score":116,"supported":13,"title":220,"abstract":222,"tags":227},[221],"artificial intelligence",[223,224,225,226,221],"深度学习","机器学习","machine learning","deep learning",[27],{"code":32,"score":19,"supported":13,"title":229,"abstract":231,"tags":232},[230],"climate change",[207,230],[207],{"code":46,"score":177,"supported":13,"title":234,"abstract":235,"tags":238},[],[236,237],"精准农业","precision agriculture",[26],{"code":240,"score":177,"supported":13,"title":241,"abstract":242,"tags":245},"I3",[],[243,244],"遥感","remote sensing",[208],{"code":247,"score":57,"supported":13,"title":248,"abstract":249,"tags":252},"H4",[],[250,251],"碳排放","carbon emission",[],{"code":254,"score":57,"supported":13,"title":255,"abstract":256,"tags":259},"H6",[],[257,258],"生物多样性","biodiversity",[],{"code":261,"score":57,"supported":13,"title":262,"abstract":263,"tags":266},"I5",[],[264,265],"物联网","internet of things",[],[268,269],"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":271,"openalex_id":273,"authors":274,"venue":197,"cited_by_count":277,"oa_url":194,"card":278,"direction":284,"ingested_from":285},"W7222092855",[275],{"name":276,"orcid":8},"Deepmala Suryavanshi",0,{"tldr":279,"method":280,"finding":281,"direction":282,"opportunity":283},"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",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":8,"source_name":293,"source_url":290,"published_at":294,"category":199,"cover_url":8,"hotness":12,"is_selected":40,"score":295,"score_detail":296,"sources":300,"tags":302,"primary_topic":306,"secondary_topics":307,"classification":313,"search_phrases":346,"slug":349,"view_count":149,"doi":350,"paper":351,"created_at":369},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":17,"substance":159,"depth":116,"authority":297,"freshness":298,"relevant":21,"comment":299},13,8,"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。",[301],{"name":293,"url":290},[26,27,303,304,305],"农业大模型","数字孪生","气候韧性农业",{"code":88,"slug":210,"name":211,"path":212,"parent_code":213},[308,309],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":261,"slug":310,"name":311,"path":312,"parent_code":213},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":40,"version":41,"method":42,"evidence":314},{"candidates":315,"agricultural_evidence":13},[316,321,326,331,336,340],{"code":88,"score":317,"supported":13,"title":318,"abstract":319,"tags":320},19,[221],[223,224,225,226,221],[27,303],{"code":46,"score":322,"supported":13,"title":323,"abstract":324,"tags":325},11,[],[236,304,237],[304,26],{"code":32,"score":12,"supported":13,"title":327,"abstract":328,"tags":330},[],[207,329,230],"气候适应",[305],{"code":261,"score":20,"supported":13,"title":332,"abstract":333,"tags":335},[],[264,265,334],"IoT",[],{"code":240,"score":57,"supported":13,"title":337,"abstract":338,"tags":339},[],[243,244],[],{"code":341,"score":65,"supported":13,"title":342,"abstract":343,"tags":345},"A7",[],[344],"间作",[],[347,348],"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":350,"openalex_id":352,"authors":353,"venue":293,"cited_by_count":277,"oa_url":290,"card":363,"direction":284,"ingested_from":285},"W7221321372",[354,357,360],{"name":355,"orcid":356},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":358,"orcid":359},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":361,"orcid":362},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":364,"method":365,"finding":366,"direction":367,"opportunity":368},"综述AI、基础模型与数字孪生如何支撑气候韧性农业，并指出关键挑战与未来方向。","文献综述，聚焦机器学习、深度学习、可解释AI、多模态基础模型与数字孪生。","最大潜力在于融合多源异构数据实现情境感知预测与自主决策，但数据质量与可解释性等仍是瓶颈。","农业人工智能与决策模型","可探索可信AI、联邦学习与自学习数字孪生在真实农田的规模化验证与语义互操作。","2026-10-11T23:30:07.465569Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":8,"source_name":376,"source_url":373,"published_at":377,"category":199,"cover_url":8,"hotness":12,"is_selected":40,"score":378,"score_detail":379,"sources":382,"tags":384,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":412,"slug":415,"view_count":149,"doi":416,"paper":417,"created_at":430},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":116,"substance":159,"depth":380,"authority":298,"freshness":20,"relevant":21,"comment":381},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。",[383],{"name":376,"url":373},[26,27,385,386,387],"数字普惠金融","农村金融","绿色金融",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":391},{"candidates":392,"agricultural_evidence":13},[393,398,403,408],{"code":46,"score":19,"supported":13,"title":394,"abstract":396,"tags":397},[395],"smart agriculture",[26,395],[26],{"code":399,"score":65,"supported":40,"title":400,"abstract":401,"tags":402},"G3",[],[],[386,385,387],{"code":32,"score":65,"supported":13,"title":404,"abstract":405,"tags":407},[],[406],"climate resilience",[],{"code":88,"score":21,"supported":40,"title":409,"abstract":410,"tags":411},[],[],[27],[413,414],"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":416,"openalex_id":418,"authors":419,"venue":376,"cited_by_count":277,"oa_url":373,"card":424,"direction":284,"ingested_from":285},"W7220700804",[420,422],{"name":421,"orcid":8},"A.V. Mardoyan",{"name":423,"orcid":8},"H.A. Sargsyan",{"tldr":425,"method":426,"finding":427,"direction":428,"opportunity":429},"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":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":8,"source_name":437,"source_url":434,"published_at":438,"category":199,"cover_url":8,"hotness":12,"is_selected":40,"score":295,"score_detail":439,"sources":442,"tags":444,"primary_topic":446,"secondary_topics":447,"classification":450,"search_phrases":494,"slug":497,"view_count":149,"doi":498,"paper":499,"created_at":512},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":116,"substance":17,"depth":116,"authority":440,"freshness":20,"relevant":21,"comment":441},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。",[443],{"name":437,"url":434},[26,27,264,304,445],"作物产量预测",{"code":88,"slug":210,"name":211,"path":212,"parent_code":213},[448,449],{"code":261,"slug":310,"name":311,"path":312,"parent_code":213},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":451},{"candidates":452,"agricultural_evidence":13},[453,458,463,467,471,478,484,490],{"code":46,"score":317,"supported":13,"title":454,"abstract":456,"tags":457},[455],"digital twin",[26,304,395,455],[304,26],{"code":88,"score":47,"supported":13,"title":459,"abstract":461,"tags":462},[460],"decision support",[221],[27],{"code":261,"score":12,"supported":13,"title":464,"abstract":465,"tags":466},[],[264,265,334],[264],{"code":32,"score":20,"supported":13,"title":468,"abstract":469,"tags":470},[],[207,329,230],[],{"code":472,"score":57,"supported":13,"title":473,"abstract":474,"tags":477},"H2",[],[475,476],"土壤","soil",[],{"code":77,"score":57,"supported":13,"title":479,"abstract":480,"tags":483},[],[481,482],"灌溉","irrigation",[],{"code":485,"score":65,"supported":13,"title":486,"abstract":487,"tags":489},"H8",[],[488],"可持续农业",[],{"code":240,"score":21,"supported":40,"title":491,"abstract":492,"tags":493},[],[],[445],[495,496],"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":498,"openalex_id":500,"authors":501,"venue":437,"cited_by_count":277,"oa_url":434,"card":507,"direction":284,"ingested_from":285},"W7219849006",[502,505],{"name":503,"orcid":504},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":506,"orcid":8},"Munish Bhatia",{"tldr":508,"method":509,"finding":510,"direction":284,"opportunity":511},"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"]