[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1380":3,"related-1380":171},{"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":31,"secondary_topics":37,"classification":50,"search_phrases":134,"slug":137,"view_count":22,"doi":138,"paper":139,"created_at":170},1380,"Harnessing Digital Twin (DT) Technology for Food Security and Climate Resilience in Sub-Saharan Africa (SSA)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102532","Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility criteria. Forty peer-reviewed studies published between 2019 and 2025 were included. Structured extraction captured bibliometric, geographic, methodological, technological, and technology readiness level (TRL) data, analysed using frequency tabulation and thematic clustering. DT agriculture publications roughly tripled between 2019 and 2023 (from three to ten studies), yet 57.5% of studies remain at TRL 2–3. Europe and East Asia dominate the evidence base; no study was led by an SSA institution, and no study reported SSA-based deployment on priority crops such as maize, cassava, sorghum, or cowpea. Internet of Things (IoT) sensor infrastructure (92.5%) and artificial intelligence\u002Fmachine learning (AI\u002FML) algorithms (52.5%) constitute the dominant technology stack. Five thematic clusters were identified: smart farming infrastructure, precision water and nutrient management, controlled environment agriculture, agrifood supply chains, and livestock and aquaculture systems. Government-funded studies tend to achieve higher TRL scores than academically funded counterparts. Synthesising these findings into a system-level, deployment-oriented analysis, we derive a conceptual SSA-DT framework. It combines offline-first edge-computing architecture, hybrid crop modelling, and multi-stakeholder interfaces, with dietary diversity and climate resilience as the two key performance indicators (KPIs), aligned with IITA’s 2024–2030 Strategy. North–South research partnerships and targeted CGIAR investment in DT technology are identified as priority actions for closing the SSA evidence gap.","撒哈拉以南非洲（SSA）尽管拥有全球约60%的未开垦耕地，却长期面临粮食不安全问题。田间试验虽能提供实证，但成本高昂且规模不足以应对日益加剧的气候与人口压力。数字孪生（DT）技术，定义为利用实时数据对物理系统进行持续、双向的虚拟复制，可支持农业食品系统的监测、建模与优化。然而，据我们所知，尚无已发表的综述从SSA粮食系统或小农生产现实的角度审视DT农业研究。本研究遵循PRISMA规范，在书目数据库中采用预定义的布尔检索和适应性PICOS资格标准进行系统综述，纳入2019至2025年间发表的40篇同行评审研究。结构化提取涵盖文献计量、地理、方法、技术及技术就绪水平（TRL）数据，并采用频次制表和主题聚类进行分析。DT农业出版物在2019至2023年间约增长两倍（从三篇增至十篇），但57.5%的研究仍处于TRL 2–3级。欧洲和东亚主导了证据基础；没有一项研究由SSA机构牵头，也没有研究报告在SSA地区针对玉米、木薯、高粱或豇豆等优先作物进行部署。物联网（IoT）传感器基础设施（92.5%）和人工智能\u002F机器学习（AI\u002FML）算法（52.5%）构成主导技术栈。研究识别出五个主题聚类：智慧农业基础设施、精准水肥管理、受控环境农业、农业食品供应链以及畜牧和水产养殖系统。政府资助的研究往往比学术资助的研究达到更高的TRL评分。将这些发现综合为系统层面、面向部署的分析，我们提出了一个概念性的SSA-DT框架。该框架结合离线优先的边缘计算架构、混合作物建模和多利益相关方接口，以膳食多样性和气候韧性作为两项关键绩效指标（KPI），与国际热带农业研究所（IITA）2024–2030年战略保持一致。南北研究伙伴关系和CGIAR对DT技术的定向投资被确定为缩小SSA证据差距的优先行动。",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,7,1,"系统综述数字孪生技术在撒哈拉以南非洲的应用，揭示研究空白并提出框架，对全球农业信息化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","非洲","粮食安全","数字孪生",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,63,69,75,82,91,99,106,113,120,127],{"code":57,"score":19,"supported":58,"title":59,"abstract":61,"tags":62},"I1",true,[60],"digital twin",[27,30,60],[30,27],{"code":39,"score":64,"supported":58,"title":65,"abstract":67,"tags":68},11,[66],"climate resilience",[66],[],{"code":32,"score":70,"supported":58,"title":71,"abstract":73,"tags":74},9,[72],"food security",[],[29],{"code":45,"score":70,"supported":58,"title":76,"abstract":77,"tags":81},[],[78,79,80],"机器学习","machine learning","artificial intelligence",[],{"code":83,"score":70,"supported":58,"title":84,"abstract":85,"tags":90},"I5",[],[86,87,88,89],"物联网","边缘计算","internet of things","IoT",[],{"code":92,"score":93,"supported":58,"title":94,"abstract":95,"tags":98},"A3",6,[],[96,97],"玉米","maize",[],{"code":100,"score":93,"supported":58,"title":101,"abstract":102,"tags":105},"A5",[],[103,104],"高粱","sorghum",[],{"code":107,"score":93,"supported":58,"title":108,"abstract":109,"tags":112},"C",[],[110,111],"畜牧","livestock",[],{"code":114,"score":93,"supported":58,"title":115,"abstract":116,"tags":119},"D",[],[117,118],"水产","aquaculture",[],{"code":121,"score":93,"supported":58,"title":122,"abstract":123,"tags":126},"I7",[],[124,125],"文献计量","bibliometric",[],{"code":128,"score":129,"supported":58,"title":130,"abstract":131,"tags":133},"G2",3,[],[132],"smallholder",[],[135,136],"数字孪生 智慧农业 粮食安全 非洲","数字孪生 智慧农业","数字孪生智慧农业粮食安全非洲-1380","10.1016\u002Fj.atech.2026.102532",{"doi":138,"openalex_id":140,"authors":141,"venue":10,"cited_by_count":161,"oa_url":162,"card":163,"direction":167,"ingested_from":169},"W7204898011",[142,145,147,150,152,155,157,159],{"name":143,"orcid":144},"Henri E. Z. Tonnang","https:\u002F\u002Forcid.org\u002F0000-0002-9424-9186",{"name":146,"orcid":9},"Francis Chianu",{"name":148,"orcid":149},"Siyabusa Mkuhlani","https:\u002F\u002Forcid.org\u002F0000-0001-7872-2036",{"name":151,"orcid":9},"Francis Muthoni",{"name":153,"orcid":154},"John Choptiany","https:\u002F\u002Forcid.org\u002F0009-0008-9961-4606",{"name":156,"orcid":9},"Franck B.N. Tonle",{"name":158,"orcid":9},"Bonoukpoe M. Sokame",{"name":160,"orcid":9},"Mercy Lung’aho",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007574\u002Fpdf",{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"系统综述数字孪生技术在撒哈拉以南非洲农业的应用，提出SSA-DT框架。","PRISMA系统综述，检索2019-2025年40篇研究，频率统计和主题聚类。","DT农业研究增长但TRL低，SSA无主导研究，提出离线优先边缘计算框架。","数字乡村与农业信息化","SSA地区数字孪生农业研究空白，可探索离线优先架构与本地作物模型结合。","openalex","2026-09-02T23:30:04.414055Z",{"total":93,"page":22,"page_size":93,"items":172},[173,243,325,414,494,550],{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":186,"tags":188,"primary_topic":192,"secondary_topics":193,"classification":199,"search_phrases":223,"slug":226,"view_count":22,"doi":227,"paper":228,"created_at":242},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。","Journal of Smart Farming and Food Security","2026-10-19T00:00:00Z",77,{"impact":17,"substance":183,"depth":184,"authority":20,"freshness":70,"relevant":22,"comment":185},20,17,"系统综述梳理地缘政治与粮食安全风险，并强调智慧农业的韧性价值，具政策与产业参考意义，但属综述性论文，非突破性成果。",[187],{"name":179,"url":176},[27,189,29,190,191],"农业供应链","精准农业","地缘政治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[194,195],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":83,"slug":196,"name":197,"path":198,"parent_code":49},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":51,"locked":14,"version":52,"method":53,"evidence":200},{"candidates":201,"agricultural_evidence":58},[202,207,212,219],{"code":32,"score":203,"supported":58,"title":204,"abstract":205,"tags":206},15,[72],[29,72],[29],{"code":57,"score":64,"supported":58,"title":208,"abstract":209,"tags":211},[],[27,190,210],"precision agriculture",[27,190],{"code":39,"score":70,"supported":58,"title":213,"abstract":214,"tags":218},[],[215,216,217],"气候变化","干旱","climate change",[],{"code":83,"score":70,"supported":58,"title":220,"abstract":221,"tags":222},[],[86,88,89],[],[224,225],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":227,"openalex_id":229,"authors":230,"venue":179,"cited_by_count":161,"oa_url":176,"card":236,"direction":240,"ingested_from":169},"W7222902031",[231,233],{"name":232,"orcid":9},"Parameswaran Mahendran",{"name":234,"orcid":235},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","智慧农业 \u002F 农业物联网","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","2026-10-19T23:30:07.850994Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":257,"primary_topic":261,"secondary_topics":262,"classification":265,"search_phrases":300,"slug":303,"view_count":304,"doi":305,"paper":306,"created_at":324},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":18,"substance":183,"depth":17,"authority":20,"freshness":253,"relevant":22,"comment":254},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。",[256],{"name":249,"url":246},[27,258,259,30,260],"农业人工智能","农业大模型","气候韧性农业",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[263,264],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":83,"slug":196,"name":197,"path":198,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":266},{"candidates":267,"agricultural_evidence":58},[268,274,278,283,287,294],{"code":45,"score":19,"supported":58,"title":269,"abstract":270,"tags":273},[80],[271,78,79,272,80],"深度学习","deep learning",[258,259],{"code":57,"score":64,"supported":58,"title":275,"abstract":276,"tags":277},[],[190,30,210],[30,27],{"code":39,"score":13,"supported":58,"title":279,"abstract":280,"tags":282},[],[215,281,217],"气候适应",[260],{"code":83,"score":70,"supported":58,"title":284,"abstract":285,"tags":286},[],[86,88,89],[],{"code":288,"score":93,"supported":58,"title":289,"abstract":290,"tags":293},"I3",[],[291,292],"遥感","remote sensing",[],{"code":295,"score":129,"supported":58,"title":296,"abstract":297,"tags":299},"A7",[],[298],"间作",[],[301,302],"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":305,"openalex_id":307,"authors":308,"venue":249,"cited_by_count":161,"oa_url":246,"card":318,"direction":240,"ingested_from":169},"W7221321372",[309,312,315],{"name":310,"orcid":311},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":313,"orcid":314},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":316,"orcid":317},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":319,"method":320,"finding":321,"direction":322,"opportunity":323},"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":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":333,"sources":336,"tags":338,"primary_topic":341,"secondary_topics":345,"classification":348,"search_phrases":389,"slug":392,"view_count":304,"doi":393,"paper":394,"created_at":413},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":18,"depth":19,"authority":334,"freshness":253,"relevant":22,"comment":335},14,"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。",[337],{"name":331,"url":328},[27,29,78,339,340],"遥感监测","农业干旱",{"code":288,"slug":342,"name":343,"path":344,"parent_code":49},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[346,347],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":349},{"candidates":350,"agricultural_evidence":58},[351,355,360,364,368,375,379,385],{"code":288,"score":203,"supported":58,"title":352,"abstract":353,"tags":354},[292],[291,292],[339],{"code":39,"score":334,"supported":58,"title":356,"abstract":358,"tags":359},[357],"drought",[216,357],[],{"code":45,"score":13,"supported":58,"title":361,"abstract":362,"tags":363},[],[271,78,79,272,80],[78],{"code":32,"score":21,"supported":58,"title":365,"abstract":366,"tags":367},[],[29,72],[29],{"code":369,"score":93,"supported":58,"title":370,"abstract":371,"tags":374},"H2",[],[372,373],"土壤","soil",[],{"code":114,"score":129,"supported":58,"title":376,"abstract":377,"tags":378},[],[117],[],{"code":380,"score":129,"supported":58,"title":381,"abstract":382,"tags":384},"H6",[],[383],"农业生态",[],{"code":57,"score":22,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[27],[390,391],"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":393,"openalex_id":395,"authors":396,"venue":331,"cited_by_count":161,"oa_url":406,"card":407,"direction":240,"ingested_from":169},"W7221081979",[397,400,403],{"name":398,"orcid":399},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":401,"orcid":402},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":404,"orcid":405},"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":408,"method":409,"finding":410,"direction":411,"opportunity":412},"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。","农业遥感与作物表型","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":422,"sources":424,"tags":426,"primary_topic":428,"secondary_topics":429,"classification":432,"search_phrases":475,"slug":478,"view_count":304,"doi":479,"paper":480,"created_at":493},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":334,"freshness":70,"relevant":22,"comment":423},"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。",[425],{"name":420,"url":417},[27,258,86,30,427],"作物产量预测",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[430,431],{"code":83,"slug":196,"name":197,"path":198,"parent_code":49},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":433},{"candidates":434,"agricultural_evidence":58},[435,440,446,450,454,458,465,471],{"code":57,"score":19,"supported":58,"title":436,"abstract":437,"tags":439},[60],[27,30,438,60],"smart agriculture",[30,27],{"code":45,"score":441,"supported":58,"title":442,"abstract":444,"tags":445},12,[443],"decision support",[80],[258],{"code":83,"score":13,"supported":58,"title":447,"abstract":448,"tags":449},[],[86,88,89],[86],{"code":39,"score":70,"supported":58,"title":451,"abstract":452,"tags":453},[],[215,281,217],[],{"code":369,"score":93,"supported":58,"title":455,"abstract":456,"tags":457},[],[372,373],[],{"code":459,"score":93,"supported":58,"title":460,"abstract":461,"tags":464},"H3",[],[462,463],"灌溉","irrigation",[],{"code":466,"score":129,"supported":58,"title":467,"abstract":468,"tags":470},"H8",[],[469],"可持续农业",[],{"code":288,"score":22,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[427],[476,477],"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":479,"openalex_id":481,"authors":482,"venue":420,"cited_by_count":161,"oa_url":417,"card":488,"direction":240,"ingested_from":169},"W7219849006",[483,486],{"name":484,"orcid":485},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":487,"orcid":9},"Munish Bhatia",{"tldr":489,"method":490,"finding":491,"direction":240,"opportunity":492},"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":495,"title":496,"url":497,"summary":498,"summary_zh":499,"content":9,"source_name":500,"source_url":497,"published_at":501,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":502,"score_detail":503,"sources":505,"tags":507,"primary_topic":508,"secondary_topics":509,"classification":512,"search_phrases":532,"slug":535,"view_count":304,"doi":536,"paper":537,"created_at":549},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":17,"substance":183,"depth":17,"authority":20,"freshness":70,"relevant":22,"comment":504},"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。",[506],{"name":500,"url":497},[27,258,86,30,190],{"code":83,"slug":196,"name":197,"path":198,"parent_code":49},[510,511],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":513},{"candidates":514,"agricultural_evidence":58},[515,520,524,528],{"code":83,"score":17,"supported":58,"title":516,"abstract":517,"tags":519},[88],[86,87,88,89,518],"edge computing",[86],{"code":57,"score":441,"supported":58,"title":521,"abstract":522,"tags":523},[],[190,30,210],[30,27,190],{"code":45,"score":441,"supported":58,"title":525,"abstract":526,"tags":527},[80],[80],[258],{"code":39,"score":70,"supported":58,"title":529,"abstract":530,"tags":531},[],[215,217,66],[],[533,534],"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":536,"openalex_id":538,"authors":539,"venue":500,"cited_by_count":161,"oa_url":497,"card":543,"direction":240,"ingested_from":169},"W7216004668",[540],{"name":541,"orcid":542},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":544,"method":545,"finding":546,"direction":547,"opportunity":548},"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":551,"title":552,"url":553,"summary":554,"summary_zh":555,"content":9,"source_name":556,"source_url":553,"published_at":557,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":558,"score_detail":559,"sources":562,"tags":564,"primary_topic":567,"secondary_topics":568,"classification":576,"search_phrases":603,"slug":606,"view_count":129,"doi":607,"paper":608,"created_at":619},3613,"AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1723457","High-volume food manufacturing in Saudi Arabia is increasingly exposed to volatile demand, short product life cycles, promotion-driven peaks, imported input dependencies, capacity bottlenecks, and the operational consequences of seasonal and religious events. These conditions make demand forecasting and production scheduling inseparable rather than sequential planning tasks. This review synthesizes research published between 2020 and 2025 on artificial intelligence-assisted demand forecasting, perishable-product planning, finite-capacity scheduling, digital-twin-enabled decision support, and food supply-chain resilience. The study develops an integrated framework in which demand sensing produces probabilistic forecasts that are translated into production decisions through capacity, shelf-life, sanitation, changeover, labour, inventory, and service constraints. Evidence indicates that machine-learning and deep-learning methods can improve forecast accuracy when promotions, weather, calendar events, regional variation, and censored demand are represented appropriately, while optimization models can convert these signals into feasible schedules that reduce changeovers, waste, shortages, and unstable resource use [1-8]. The review also identifies a persistent implementation gap: forecasting studies often stop at predictive accuracy, whereas scheduling studies frequently assume demand inputs are fixed and reliable. The proposed framework closes this gap by linking forecast uncertainty, rolling-horizon scheduling, execution feedback, and food-security outcomes. For Saudi manufacturers, the value of this integration lies not only in lower cost, but also in improving product availability, freshness, localized processing capacity, shock recovery, and evidence-based management under Vision 2030. The paper concludes with a staged implementation roadmap and research priorities for explainability, cross-site scheduling, event-aware forecasting, data governance, and resilient planning.","沙特阿拉伯的大规模食品制造日益面临需求波动、产品生命周期短、促销驱动的峰值、进口原料依赖、产能瓶颈以及季节性和宗教事件的运营影响。这些条件使得需求预测与生产调度成为不可分割的任务，而非顺序规划任务。本文综述了2020年至2025年间发表的相关研究，涵盖人工智能辅助需求预测、易腐产品规划、有限产能调度、数字孪生赋能的决策支持以及食品供应链韧性。研究构建了一个集成框架，其中需求感知产生概率预测，并通过产能、保质期、卫生、换产、劳动力、库存和服务约束转化为生产决策。证据表明，当促销、天气、日历事件、区域差异和截尾需求得到适当表征时，机器学习和深度学习方法能够提高预测精度，而优化模型可将这些信号转化为可行的调度方案，从而减少换产、浪费、缺货和不稳定的资源使用[1-8]。本综述还指出了一个持续存在的实施缺口：预测研究往往止步于预测精度，而调度研究则常常假设需求输入是固定且可靠的。所提出的框架通过将预测不确定性、滚动时域调度、执行反馈和食品安全结果联系起来，弥合了这一缺口。对于沙特制造商而言，这种集成的价值不仅在于降低成本，还在于改善产品可得性、新鲜度、本地化加工能力、冲击恢复能力以及“2030愿景”下的循证管理。本文最后提出了分阶段实施路线图以及可解释性、跨厂区调度、事件感知预测、数据治理和韧性规划方面的研究优先事项。","Iconic Research and Engineering Journals","2026-09-25T00:00:00Z",60,{"impact":441,"substance":17,"depth":560,"authority":93,"freshness":253,"relevant":22,"comment":561},16,"综述性论文，提出AI需求预测与生产排程整合框架，对智慧农业与粮食安全有参考价值，但属境外研究且期刊权威性一般。",[563],{"name":556,"url":553},[27,258,29,565,566],"供应链韧性","生产调度",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[569,575],{"code":570,"slug":571,"name":572,"path":573,"parent_code":574},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":577},{"candidates":578,"agricultural_evidence":58},[579,584,588,592,597],{"code":570,"score":64,"supported":58,"title":580,"abstract":582,"tags":583},[581],"food manufacturing",[581],[],{"code":45,"score":13,"supported":58,"title":585,"abstract":586,"tags":587},[],[271,78,80,443],[258],{"code":32,"score":70,"supported":58,"title":589,"abstract":590,"tags":591},[72],[],[29],{"code":57,"score":593,"supported":58,"title":594,"abstract":595,"tags":596},4,[],[30],[27],{"code":598,"score":129,"supported":58,"title":599,"abstract":600,"tags":602},"E4",[],[601],"食品安全",[],[604,605],"沙特阿拉伯 食品制造 需求预测","Vision 2030 粮食安全","沙特阿拉伯食品制造需求预测-3613","10.64388\u002Firev10i3-1723457",{"doi":607,"openalex_id":609,"authors":610,"venue":556,"cited_by_count":161,"oa_url":613,"card":614,"direction":240,"ingested_from":169},"W7214295236",[611],{"name":612,"orcid":9},"Atif Maqbool Syed","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723457.pdf",{"tldr":615,"method":616,"finding":617,"direction":322,"opportunity":618},"综述2020-2025年AI需求预测与生产调度研究，提出沙特食品制造整合框架以保障Vision 20","文献综述2020-2025年AI预测、易腐品规划、有限产能调度与数字孪生研究。","预测研究止于精度、调度研究假设需求固定，整合框架可弥合实施鸿沟并提升粮食安全。","可研究事件感知预测与滚动调度耦合、跨工厂协同及可解释AI在食品供应链韧性中的应用。","2026-09-27T23:30:14.516185Z"]