[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4600":3,"related-4600":104},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":84,"slug":87,"view_count":22,"doi":88,"paper":89,"created_at":103},4600,"Optimization model of agricultural economic structure based on LSTM","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09594-x","With the development of artificial intelligence (AI) and agricultural big data, the adjustment of agricultural economic structure has shifted from empirical judgment to data-driven decision-making. This study uses the provincial panel data of agricultural economy in Hubei Province from 2010 to 2023 to construct a comprehensive framework of prediction and optimization. The forecasting layer uses the Long Short-Term Memory (LSTM) model to capture the dynamic relationship among agricultural GDP growth rate, main crop output, rural employment structure, agricultural product price index, financial subsidies and agricultural infrastructure investment. The optimization layer uses the prediction results as parameters. It defines three objectives. overall agricultural benefit, structural deviation and food security risk. It also includes restrictions on cultivated land area, budget, labor supply and food security. Based on these backgrounds, the adjustment scheme of agricultural economic structure was formulated. The results show that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and r of LSTM model are 0.32, 0.45 and 0.91 respectively. Its prediction performance is better than autoregressive moving average (ARIMA), back propagation neural network (BPNN), support vector regression (SVR) and random forest (RF). These findings show that the combination of multivariate collaborative forecasting and structural optimization constraints can improve the efficiency of agricultural resource allocation. This method also provides quantitative support for rural revitalization, food security and digital agriculture policy design.","随着人工智能（AI）与农业大数据的发展，农业经济结构调整已从经验判断转向数据驱动决策。本研究利用2010—2023年湖北省农业经济省级面板数据，构建了预测与优化的综合框架。预测层采用长短期记忆（LSTM）模型，捕捉农业GDP增长率、主要作物产量、农村就业结构、农产品价格指数、财政补贴与农业基础设施投资之间的动态关系。优化层以预测结果作为参数，设定三个目标：农业整体效益、结构性偏差与粮食安全风险，并纳入耕地面积、预算、劳动力供给与粮食安全等约束条件。在此基础上，制定农业经济结构调整方案。结果表明，LSTM模型的平均绝对误差（MAE）、均方根误差（RMSE）与r分别为0.32、0.45和0.91，其预测性能优于自回归移动平均（ARIMA）、反向传播神经网络（BPNN）、支持向量回归（SVR）与随机森林（RF）。这些发现表明，多变量协同预测与结构优化约束的结合能够提高农业资源配置效率。该方法也为乡村振兴、粮食安全与数字农业政策设计提供了量化支持。",null,"Discover Applied Sciences","2026-10-09T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,13,9,1,"基于湖北省级面板数据构建LSTM预测与多目标优化框架，方法对比充分、结论有量化支撑，对数字农业与乡村振兴政策设计具参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","农业人工智能","粮食安全","农业经济结构","LSTM预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,65,72,78],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[29,50],"food security",[29],{"code":53,"score":46,"supported":47,"title":54,"abstract":55,"tags":57},"I1",[],[27,56],"digital agriculture",[27],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"I2",4,[],[63],"artificial intelligence",[28],{"code":66,"score":67,"supported":47,"title":68,"abstract":69,"tags":71},"G",3,[],[70],"乡村振兴",[],{"code":73,"score":67,"supported":47,"title":74,"abstract":75,"tags":77},"I6",[],[76],"农业大数据",[],{"code":79,"score":67,"supported":47,"title":80,"abstract":81,"tags":83},"J1",[],[82],"农业政策",[],[85,86],"湖北 农业经济结构 LSTM","农业经济结构 优化模型","湖北农业经济结构LSTM-4600","10.1007\u002Fs42452-026-09594-x",{"doi":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":101,"ingested_from":102},"W7221088951",[92],{"name":93,"orcid":9},"Xue Gong",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"用LSTM预测湖北农业经济指标，并构建多目标优化模型调整农业结构。","LSTM预测2010-2023年湖北面板数据，结合多目标优化与约束条件。","LSTM预测精度优于ARIMA、BPNN等，优化方案可提升农业资源配置效率。","农业人工智能与决策模型","可探索将LSTM与多目标优化耦合框架推广至其他省份或作物类型，并引入实时数据流。","数字乡村与农业信息化","openalex","2026-10-10T23:30:34.807708Z",{"total":105,"page":22,"page_size":105,"items":106},6,[107,175,246,326,397,452],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":127,"search_phrases":152,"slug":155,"view_count":94,"doi":156,"paper":157,"created_at":174},5491,"Analysis of the Impact of Digital Technology on the Resilience of Anhui’s Agricultural Industry Chain: Threshold Effect of Relative Grain Yield","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010656","Improving the resilience of the agricultural industry chain to frequent external shocks and the intertwined agricultural production risks is critical for ensuring national food security and sustainable agricultural development. Taking the 16 prefecture-level cities in Anhui Province—a major grain-producing region in China—as the research sample, two composite proxy indices were constructed in this study; namely, an agricultural industry chain resilience index built upon aggregate official statistics (rather than direct observed shock-response outcomes) and an index capturing broad rural digital development instead of purely agriculture-specific digital adoption. Based on balanced panel data from 2015 to 2024, two-way fixed-effect, mediation-effect, and panel-threshold models were adopted in this study to examine the association, mediating mechanism, regional heterogeneity, and threshold characteristics conditioned on the relative grain yield. The empirical results show that the rural-oriented digital development index is significantly and robustly positively associated with our composite agricultural industry chain resilience proxy. Although agricultural TFP exhibits a statistically detectable partial mediating pattern in this correlational relationship, the economic magnitude of this indirect channel is extremely modest. As such, this finding indicates a potential theoretical transmission pathway rather than providing definitive empirical evidence for a causal mechanism. The promotion effect exhibits clear regional heterogeneity, with the strongest effect in Northern Anhui, followed by Central Anhui, while the coefficient is positive but insignificant in Southern Anhui. Furthermore, a single-threshold effect of the relative grain yield is identified; that is, the positive association between rural-oriented digital development and resilience proxy cannot be statistically detected when the grain yield remains below the threshold. Only after the agricultural grain yield increases can digital-related rural development release its resilience-related empowering dividends. We note that both composite indices are subject to measurement constraints linked to the local agricultural economic scale and broad rural digital conditions; these limitations are further discussed in the paper. This study provides empirical correlational evidence for the implementation of differentiated digital agriculture policies in major grain-producing provinces.","提升农业产业链对频繁外部冲击与交织叠加的农业生产风险的韧性，对保障国家粮食安全和农业可持续发展至关重要。本研究以中国粮食主产省安徽省的16个地级市为研究样本，构建了两个复合代理指标：一是基于官方统计数据总量（而非直接观测的冲击—响应结果）构建的农业产业链韧性指数，二是刻画农村数字化广泛发展（而非单纯农业领域数字化应用）的指数。基于2015—2024年平衡面板数据，本研究采用双向固定效应模型、中介效应模型和面板门槛模型，考察二者之间的关联、中介机制、区域异质性以及以相对粮食产量为条件变量的门槛特征。实证结果表明，农村导向的数字化发展指数与本研究的农业产业链韧性复合代理指标之间存在显著且稳健的正向关联。尽管农业全要素生产率在这一相关关系中呈现出统计上可检测的部分中介模式，但该间接渠道的经济量级极为有限。因此，这一发现指示的是一种潜在的理论传导路径，而非为因果机制提供确凿的实证证据。其促进效应表现出明显的区域异质性，皖北效应最强，皖中次之，皖南系数为正但不显著。此外，还识别出相对粮食产量的单一门槛效应，即当粮食产量低于门槛值时，农村导向的数字化发展与韧性代理指标之间的正向关联在统计上无法被检测到；只有在农业粮食产量提高之后，数字相关农村发展才能释放其韧性赋能红利。我们注意到，两个复合指数均受到与地方农业经济规模和农村数字化总体状况相关的测度约束；这些局限性在文中进一步讨论。本研究为粮食主产省实施差异化数字农业政策提供了实证相关性证据。","Sustainability","2026-10-20T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":117,"relevant":22,"comment":118},8,"基于安徽16市面板数据的实证研究，方法规范、结论有区域异质性价值，但属省级学术论文，公共影响有限，适合作为主题页聚合素材而非头条精选。",[120],{"name":113,"url":110},[27,122,29,123,124],"数字乡村","农业产业链韧性","安徽农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":128},{"candidates":129,"agricultural_evidence":47},[130,134,138,144,148],{"code":33,"score":46,"supported":47,"title":131,"abstract":132,"tags":133},[],[29,50],[29],{"code":53,"score":46,"supported":47,"title":135,"abstract":136,"tags":137},[],[27,56],[27],{"code":66,"score":105,"supported":47,"title":139,"abstract":140,"tags":143},[],[141,142],"农村发展","rural development",[],{"code":79,"score":67,"supported":47,"title":145,"abstract":146,"tags":147},[],[82],[],{"code":73,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},[],[],[122],[153,154],"安徽 农业产业链韧性 数字技术","安徽 粮食产量 数字乡村","安徽农业产业链韧性数字技术-5491","10.3390\u002Fsu182010656",{"doi":156,"openalex_id":158,"authors":159,"venue":113,"cited_by_count":94,"oa_url":110,"card":169,"direction":101,"ingested_from":102},"W7223005655",[160,163,165,167],{"name":161,"orcid":162},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":164,"orcid":9},"Yuyan Li",{"name":166,"orcid":9},"张国亮",{"name":168,"orcid":9},"Yaoyao Li",{"tldr":170,"method":171,"finding":172,"direction":101,"opportunity":173},"以安徽16市面板数据检验农村数字发展对农业产业链韧性的影响及粮食产量门槛效应。","2015—2024年安徽16市平衡面板，双向固定效应、中介效应与面板门槛模型。","数字发展显著提升产业链韧性，存在粮食相对产量单门槛，且皖北效应最强、皖南不显著。","可细化数字技术指标、引入真实冲击响应韧性测度，并探究门槛形成机制与皖南失效原因。","2026-10-21T23:30:29.219265Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":9,"content":9,"source_name":180,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":181,"is_selected":14,"score":182,"score_detail":183,"sources":190,"tags":195,"primary_topic":199,"secondary_topics":200,"classification":201,"search_phrases":229,"slug":232,"view_count":105,"doi":233,"paper":234,"created_at":245},4969,"Digital technology, agricultural resilience, and food security: A mini review","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.2002109\u002Ffull","Shen在《Frontiers in Sustainable Food Systems》(2026)发表的综述综合了国际数字农业文献与中国'数字经济—农业经济韧性'研究两条主线。提出三点发现:一是数字技术通过不同机制强化食物安全的四个维度——可获得性(生产力与投入效率)、可及性(市场信息与数字金融)、稳定性(溯源、预警与保险)、利用性(溯源、营养信息与移动支付);二是准自然实验证据显示数字基础设施、数字金融和人工智能主要通过社会化服务、土地流转、产业融合、人力资本而非仅'采纳'提升粮食生产与价值链韧性;三是收益分布不均,空间溢出方向取决于吸收能力、市场整合与要素流动性。","Frontiers in Sustainable Food Systems 2026",25,77,{"impact":184,"substance":185,"depth":186,"authority":187,"freshness":188,"relevant":22,"comment":189},22,21,18,14,2,"核心期刊综述，系统梳理数字技术强化粮食安全四维度的机制与准自然实验证据，兼具国际文献与中国经验，但发布日期不明、时效性偏弱，适合作为主题页深度聚合素材。",[191,192],{"name":180,"url":178},{"name":193,"url":194},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.2002109",[27,29,196,197,198],"农业韧性","空间溢出","数字金融",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":202},{"candidates":203,"agricultural_evidence":47},[204,208,215,219,224],{"code":33,"score":21,"supported":47,"title":205,"abstract":206,"tags":207},[50],[],[29],{"code":209,"score":105,"supported":47,"title":210,"abstract":211,"tags":214},"G2",[],[212,213],"土地流转","社会化服务",[],{"code":53,"score":60,"supported":47,"title":216,"abstract":217,"tags":218},[],[27],[27],{"code":220,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},"G3",[],[],[198],{"code":225,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},"I4",[],[],[197],[230,231],"Frontiers in Sustainable Food Systems 数字农业","数字技术 农业韧性 粮食安全","FrontiersinSustainableFoodSystems数字农业-4969","10.3389\u002Ffsufs.2026.2002109\u002Ffull",{"doi":233,"openalex_id":235,"authors":236,"venue":193,"cited_by_count":94,"oa_url":194,"card":240,"direction":101,"ingested_from":102},"W7223134273",[237],{"name":238,"orcid":239},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915",{"tldr":241,"method":242,"finding":243,"direction":101,"opportunity":244},"综述数字技术通过多机制强化食物安全四维度并提升农业韧性，但收益分布不均。","综合国际数字农业文献与中国数字经济—农业经济韧性研究两条主线。","数字技术经社会化服务、土地流转等提升韧性，收益不均且空间溢出取决于吸收能力。","可探究数字技术收益不均的成因及吸收能力、市场整合对空间溢出方向的调节机制。","2026-10-15T00:06:08.890950Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":252,"source_url":249,"published_at":253,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":265,"classification":277,"search_phrases":308,"slug":311,"view_count":67,"doi":312,"paper":313,"created_at":325},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":256,"substance":186,"depth":17,"authority":105,"freshness":117,"relevant":22,"comment":257},12,"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。",[259],{"name":252,"url":249},[261,28,29,262,263],"智慧农业","供应链韧性","生产调度",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[266,272],{"code":267,"slug":268,"name":269,"path":270,"parent_code":271},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":59,"slug":273,"name":274,"path":275,"parent_code":276},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":278},{"candidates":279,"agricultural_evidence":47},[280,286,293,297,302],{"code":267,"score":281,"supported":47,"title":282,"abstract":284,"tags":285},11,[283],"food manufacturing",[283],[],{"code":59,"score":13,"supported":47,"title":287,"abstract":288,"tags":292},[],[289,290,63,291],"深度学习","机器学习","decision support",[28],{"code":33,"score":21,"supported":47,"title":294,"abstract":295,"tags":296},[50],[],[29],{"code":53,"score":60,"supported":47,"title":298,"abstract":299,"tags":301},[],[300],"数字孪生",[261],{"code":303,"score":67,"supported":47,"title":304,"abstract":305,"tags":307},"E4",[],[306],"食品安全",[],[309,310],"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":312,"openalex_id":314,"authors":315,"venue":252,"cited_by_count":94,"oa_url":318,"card":319,"direction":324,"ingested_from":102},"W7214295236",[316],{"name":317,"orcid":9},"Atif Maqbool Syed","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723457.pdf",{"tldr":320,"method":321,"finding":322,"direction":99,"opportunity":323},"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。","智慧农业 \u002F 农业物联网","2026-09-27T23:30:14.516185Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":9,"source_name":332,"source_url":329,"published_at":333,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":337,"tags":339,"primary_topic":342,"secondary_topics":343,"classification":350,"search_phrases":379,"slug":382,"view_count":383,"doi":384,"paper":385,"created_at":396},2939,"Climate Change and Food Security in Africa: Harnessing Responsible AI for Sustainable Agricultural Transformation","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no9.2025.pg79.102","Africa stands at the crossroads of two defining 21st-century challenges: climate change and food insecurity. The continents agricultural systems are increasingly disrupted by rising temperatures, erratic rainfall, and extreme weather events, exacerbating hunger, poverty, and rural vulnerability. At the same time, Artificial Intelligence (AI) presents transformative potential to support climate adaptation, improve agricultural resilience, and enhance food systems governance across Africa. This study undertakes a qualitative content analysis and critical synthesis of 25 peer-reviewed articles, institutional reports, and working papers to examine how AI is currently applied and could be more effectively leveraged for climate-resilient agriculture and food security on the continent. Findings reveal a growing body of innovations, from AIdriven weather forecasting and early warning systems to precision agriculture and supply chain optimization. Yet the study identifies persistent structural limitations, including unreliable data infrastructure, low digital capacity in rural areas, and governance gaps around ethical deployment, data ownership, and equitable access. Through African case studies, the paper explores how localized AI solutions when supported by inclusive policies, multi-stakeholder collaboration, and responsible innovation frameworks can mitigate climate-induced food shocks and drive sustainable development. The paper argues for a strategic alignment between AI deployment and national adaptation plans, emphasizing the need for stronger public-private partnerships, investments in AI-ready infrastructure, and the development of ethical and contextsensitive governance mechanisms. In doing so, it offers a roadmap for policymakers, development agencies, and researchers seeking to harness AI not just as a technological tool, but as a catalyst for systemic transformation in African food systems in the era of climate change.","非洲正处在21世纪两大决定性挑战的交汇点：气候变化与粮食不安全。随着气温上升、降雨异常和极端天气事件频发，非洲大陆的农业系统日益受到扰乱，饥饿、贫困和农村脆弱性问题不断加剧。与此同时，人工智能（AI）展现出变革性潜力，可支持非洲的气候适应、提升农业韧性并改善粮食系统治理。本研究对25篇同行评审论文、机构报告和工作论文进行了定性内容分析与批判性综合，考察AI当前在非洲大陆的应用方式，以及如何更有效地将其用于气候韧性农业和粮食安全。研究发现，相关创新不断涌现，涵盖AI驱动的天气预报和预警系统、精准农业以及供应链优化等领域。然而，研究也识别出持续存在的结构性制约，包括数据基础设施不可靠、农村地区数字能力薄弱，以及围绕伦理部署、数据所有权和公平获取的治理缺口。通过非洲案例研究，本文探讨了在包容性政策、多利益相关方协作和负责任创新框架支持下，本地化的AI解决方案如何能够缓解气候引发的粮食冲击并推动可持续发展。本文主张将AI部署与国家适应计划进行战略对接，强调需要加强公私伙伴关系、投资于AI就绪型基础设施，并建立合乎伦理且情境敏感的治理机制。由此，本文为政策制定者、发展机构和研究人员提供了一份路线图，旨在气候变化时代将AI不仅作为技术工具，更作为非洲粮食系统系统性转型的催化剂加以利用。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-16T00:00:00Z",76,{"impact":186,"substance":18,"depth":19,"authority":20,"freshness":117,"relevant":22,"comment":336},"系统综述25篇文献，梳理AI在非洲气候韧性农业中的应用与治理缺口，对农业信息化有参考价值，但属境外区域研究，公共影响有限。",[338],{"name":332,"url":329},[261,28,29,340,341],"非洲农业","气候适应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[344],{"code":345,"slug":346,"name":347,"path":348,"parent_code":349},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":351},{"candidates":352,"agricultural_evidence":47},[353,359,364,370,374],{"code":345,"score":186,"supported":47,"title":354,"abstract":356,"tags":358},[355],"climate change",[357,341,355],"气候变化",[341],{"code":33,"score":360,"supported":47,"title":361,"abstract":362,"tags":363},15,[50],[29,50],[29],{"code":53,"score":46,"supported":47,"title":365,"abstract":366,"tags":369},[],[367,368],"精准农业","precision agriculture",[261],{"code":59,"score":60,"supported":47,"title":371,"abstract":372,"tags":373},[],[63],[28],{"code":375,"score":22,"supported":14,"title":376,"abstract":377,"tags":378},"J3",[],[],[340],[380,381],"非洲 农业人工智能 粮食安全","AI 气候韧性农业 非洲","非洲农业人工智能粮食安全-2939",5,"10.56201\u002Fijaes.vol.11.no9.2025.pg79.102",{"doi":384,"openalex_id":386,"authors":387,"venue":332,"cited_by_count":94,"oa_url":390,"card":391,"direction":324,"ingested_from":102},"W7213462904",[388],{"name":389,"orcid":9},"Izuchukwu Adamaagashi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 9 2025\u002FClimate Change and Food Security 79-102.pdf",{"tldr":392,"method":393,"finding":394,"direction":99,"opportunity":395},"通过定性分析25篇文献，探讨如何负责任地利用AI推动非洲气候适应型农业与粮食安全。","对25篇同行评议论文、机构报告和工作论文进行定性内容分析与批判性综合。","AI在天气预报、精准农业等方面有创新，但面临数据基础设施差、农村数字能力低和治理缺口等结构性限制。","可研究非洲本地化AI解决方案的伦理治理框架与公私合作模式，填补数据所有权和公平获取的研究空白。","2026-09-19T23:30:19.482449Z",{"id":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":9,"source_name":403,"source_url":400,"published_at":404,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":412,"secondary_topics":413,"classification":414,"search_phrases":429,"slug":432,"view_count":67,"doi":433,"paper":434,"created_at":451},1746,"Digitization in Agriculture and Food Security in Sub-Saharan Africa: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fajbar.v13i1.032","Digitization is increasingly recognized as a transformative force in Sub-Saharan Africa’s (SSA) agricultural sector, with significant implications for productivity enhancement and food security improvement. This systematic review examines the role of digital technologies in agriculture, focusing on their contributions to food availability, accessibility, utilization, and stability across SSA. The study adopts a systematic literature review (SLR) methodology, drawing evidence from peer-reviewed journals and reports published by institutions such as the World Bank, FAO, IFAD, and the African Development Bank between 2015 and 2025. Findings reveal that digital agriculture interventions, including mobile advisory systems, precision agriculture tools, digital financial services, and e-market platforms, significantly improve agricultural productivity, reduce transaction costs, strengthen market integration, and enhance farmers’ access to financial services. However, adoption remains uneven due to infrastructural deficits, digital illiteracy, gender inequality, and weak institutional frameworks. The review concludes that while digitization holds strong potential to transform food systems in SSA, its effectiveness depends on enabling environments such as infrastructure development, inclusive policies, and capacity building. The study recommends strengthening digital infrastructure, improving literacy programs, and expanding public–private partnerships to accelerate inclusive digital transformation in agriculture.","数字化日益被视为撒哈拉以南非洲（SSA）农业部门的变革性力量，对提高生产力和改善粮食安全具有重大意义。本系统综述考察了数字技术在农业中的作用，重点关注其对撒哈拉以南非洲粮食可得性、可及性、利用和稳定性的贡献。研究采用系统文献综述（SLR）方法，借鉴了2015年至2025年间同行评审期刊以及世界银行、联合国粮农组织（FAO）、国际农业发展基金（IFAD）和非洲开发银行等机构发布的报告中的证据。研究结果表明，包括移动咨询系统、精准农业工具、数字金融服务和电子市场平台在内的数字农业干预措施，显著提高了农业生产力，降低了交易成本，加强了市场整合，并增强了农民获取金融服务的机会。然而，由于基础设施不足、数字文盲、性别不平等和制度框架薄弱，数字技术的采用仍不均衡。本综述得出结论：尽管数字化在改变撒哈拉以南非洲粮食系统方面具有巨大潜力，但其有效性取决于有利环境，如基础设施发展、包容性政策和能力建设。研究建议加强数字基础设施建设，改善扫盲计划，并扩大公私伙伴关系，以加速农业领域的包容性数字化转型。","Journal of Biotechnology and Agricultural Research","2026-09-04T00:00:00Z",70,{"impact":186,"substance":18,"depth":186,"authority":256,"freshness":188,"relevant":22,"comment":407},"系统综述非洲数字农业对粮食安全的影响，证据扎实，但时效性低，适合精选。",[409],{"name":403,"url":400},[27,411,29,340],"系统综述",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":415},{"candidates":416,"agricultural_evidence":47},[417,421,425],{"code":33,"score":360,"supported":47,"title":418,"abstract":419,"tags":420},[50],[29,50],[29],{"code":53,"score":13,"supported":47,"title":422,"abstract":423,"tags":424},[],[367,27,368,56],[27],{"code":375,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[340],[430,431],"数字农业 粮食安全 系统综述 非洲农业","数字农业 粮食安全","数字农业粮食安全系统综述非洲农业-1746","10.70382\u002Fajbar.v13i1.032",{"doi":433,"openalex_id":435,"authors":436,"venue":403,"cited_by_count":94,"oa_url":400,"card":446,"direction":101,"ingested_from":102},"W7208759473",[437,440,442,444],{"name":438,"orcid":439},"Funso O. Alabuja","https:\u002F\u002Forcid.org\u002F0000-0002-7579-1041",{"name":441,"orcid":9},"Ejiofor D. Chukwubuike",{"name":443,"orcid":9},"Lateefah O. Lama-Abdul",{"name":445,"orcid":9},"Olabiyi Fatimah Adebisi",{"tldr":447,"method":448,"finding":449,"direction":101,"opportunity":450},"系统综述撒哈拉以南非洲农业数字化对粮食安全的影响，发现其提升生产力但采用不均。","系统文献综述，分析2015-2025年同行评审文献及国际机构报告。","数字农业提高生产力、降低交易成本，但受基础设施、数字素养等制约。","可研究数字农业采用不均的驱动因素，如性别差异、制度障碍，探索包容性数字化路径。","2026-09-05T23:30:35.595189Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":404,"category":12,"cover_url":9,"hotness":181,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":466,"primary_topic":468,"secondary_topics":469,"classification":470,"search_phrases":496,"slug":499,"view_count":22,"doi":500,"paper":501,"created_at":513},1717,"AI-Driven Smart Agriculture And Food Security In Developing Countries: A Comparative Perspective","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303847","Agriculture remains a cornerstone of economic development and food security in many developing countries. However, the sector continues to face challenges such as climate change, declining natural resources, labour shortages, and increasing production costs. Recent advances in artificial intelligence (AI) and smart agriculture provide new opportunities to improve agricultural productivity, optimize resource use, and strengthen food security. This study examines the role of AI-driven smart agriculture in enhancing food security through a comparative analysis of India, China, Brazil, Vietnam, Kenya, and Indonesia. The findings reveal considerable differences in AI readiness and the adoption of smart agricultural technologies among the countries. Nations with stronger digital infrastructure and higher AI readiness demonstrate greater adoption of precision farming practices and better food security outcomes. The study concludes that expanding digital infrastructure, improving farmers' access to AI-based technologies, strengthening technical capacity, and encouraging collaboration among governments, research institutions, and agritech firms are essential for achieving sustainable agricultural development and improving food security in developing countries. The novelty of this study lies in integrating AI readiness, digital infrastructure, precision farming adoption, and food security into a comparative analysis of six developing countries using 2024 secondary data.","农业仍然是许多发展中国家经济发展和粮食安全的基石。然而，该部门持续面临气候变化、自然资源衰退、劳动力短缺以及生产成本上升等挑战。人工智能（AI）和智慧农业的最新进展为提高农业生产率、优化资源利用以及增强粮食安全提供了新的机遇。本研究通过对印度、中国、巴西、越南、肯尼亚和印度尼西亚的比较分析，探讨了AI驱动的智慧农业在提升粮食安全方面的作用。研究结果显示，各国在AI就绪度和智慧农业技术采用方面存在显著差异。数字基础设施更完善、AI就绪度更高的国家，在精准农业实践的采用率和粮食安全成果方面表现更优。研究得出结论：扩大数字基础设施、提升农民获取AI技术的机会、加强技术能力，并鼓励政府、研究机构和农业科技企业之间的合作，对于实现发展中国家的可持续农业发展及改善粮食安全至关重要。本研究的创新之处在于，利用2024年二手数据，将AI就绪度、数字基础设施、精准农业采用率及粮食安全整合到对六个发展中国家的比较分析中。","International Journal for Research Trends in Social Science & Humanities.",69,{"impact":186,"substance":18,"depth":186,"authority":13,"freshness":67,"relevant":22,"comment":461},"跨国比较研究，提供AI农业应用与粮食安全的新数据，但时效性较低。",[463,464],{"name":458,"url":455},{"name":458,"url":465},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303846",[261,28,29,467],"发展中国家",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":471},{"candidates":472,"agricultural_evidence":47},[473,478,482,486,490],{"code":53,"score":186,"supported":47,"title":474,"abstract":476,"tags":477},[475],"smart agriculture",[261,367,475],[261],{"code":33,"score":360,"supported":47,"title":479,"abstract":480,"tags":481},[50],[29,50],[29],{"code":345,"score":105,"supported":47,"title":483,"abstract":484,"tags":485},[],[357,355],[],{"code":59,"score":60,"supported":47,"title":487,"abstract":488,"tags":489},[],[63],[28],{"code":491,"score":67,"supported":47,"title":492,"abstract":493,"tags":495},"H8",[],[494],"可持续农业",[],[497,498],"农业人工智能 发展中国家 智慧农业 粮食安全","农业人工智能 发展中国家","农业人工智能发展中国家智慧农业粮食安全-1717","10.5281\u002Fzenodo.22303847",{"doi":500,"openalex_id":502,"authors":503,"venue":458,"cited_by_count":94,"oa_url":455,"card":508,"direction":324,"ingested_from":102},"W7208717815",[504,506],{"name":505,"orcid":9},"Dr.V. KALEESWARI",{"name":507,"orcid":9},"Dr. P. Anita",{"tldr":509,"method":510,"finding":511,"direction":99,"opportunity":512},"比较六国AI驱动智慧农业对粮食安全的影响，发现数字基础设施和AI准备度是关键。","基于2024年二手数据，对印度、中国等六国进行AI准备度、数字基础设施与精准农业","数字基础设施强、AI准备度高的国家，精准农业采纳多，粮食安全结果更好。","可深入研究发展中国家数字基础设施与AI准备度对精准农业采纳的因果机制，或构建评估框架指导政策。","2026-09-05T23:30:08.783371Z"]