[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2653":3,"related-2653":114},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":70,"slug":73,"view_count":21,"doi":74,"paper":75,"created_at":113},2653,"Artificial intelligence and food insecurity: opportunities, risks, and equity pathways for the Global South","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2730680","Food insecurity affects 691–783 million people globally, with disproportionate impacts on low-income and middle-income countries, marginalized groups, and conflict-affected populations. Artificial intelligence (AI) presents unprecedented opportunities to address food security challenges through precision agriculture, supply chain optimization, and early warning systems, achieving up to 91% accuracy in crop yield prediction and reducing food waste by 22%. Its deployment risks perpetuating existing inequities. Drawing on a systematic literature search of 64 peer-reviewed studies across Scopus, Web of Science, PubMed, AGRIS, and IEEE Xplore (2015–2025), this review examines AI’s double-edged role in food systems through a comprehensive framework encompassing inclusive data practices, equitable governance, participatory design, and local capacity building. Key risks include data colonialism, algorithmic bias favoring industrial agriculture, the digital exclusion of vulnerable communities, and technological dependency that threatens food sovereignty. However, evidence from successful implementations in sub-Saharan Africa demonstrates that inclusive approaches can enhance productivity while promoting gender equality and environmental sustainability. The study concludes that AI’s impact of AI fundamentally depends on the design principles and governance frameworks that prioritize equity. Without inclusive implementation strategies, AI may reinforce hunger gaps; conversely, equity-centered approaches can contribute to resilient, just, and food-secure systems for all populations.","粮食不安全影响着全球6.91亿至7.83亿人，对低收入和中等收入国家、边缘化群体以及受冲突影响人口的影响尤为严重。人工智能（AI）通过精准农业、供应链优化和早期预警系统，为解决粮食安全挑战提供了前所未有的机遇，在作物产量预测中实现了高达91%的准确率，并将粮食浪费减少了22%。然而，其部署也可能固化现有的不平等。本文基于对Scopus、Web of Science、PubMed、AGRIS和IEEE Xplore数据库中64项同行评审研究（2015—2025年）的系统性文献检索，通过一个涵盖包容性数据实践、公平治理、参与式设计和本地能力建设的综合框架，审视了AI在粮食系统中的双刃剑作用。主要风险包括数据殖民主义、偏向工业化农业的算法偏见、对脆弱社区的数字排斥，以及威胁粮食主权的技术依赖。然而，来自撒哈拉以南非洲成功实施的证据表明，包容性方法可以在提高生产力的同时促进性别平等和环境可持续性。研究得出结论，AI的影响从根本上取决于优先考虑公平的设计原则和治理框架。缺乏包容性实施策略，AI可能加剧饥饿差距；反之，以公平为中心的方法可以为所有人群构建具有韧性、公正且粮食安全的体系。",null,"Cogent Food & Agriculture","2026-09-14T00:00:00Z","论文",10,false,83,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,13,8,1,"基于64篇文献的系统综述，系统梳理AI在粮食安全中的双刃剑效应与公平治理路径，数据与框架均有实质增量，对农业信息化政策设计具参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","粮食安全","数字鸿沟","全球南方",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,65],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},12,true,[48],"artificial intelligence",[48],[27],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"A8",7,[],[28,56],"food security",[28],{"code":59,"score":53,"supported":46,"title":60,"abstract":61,"tags":64},"I1",[],[62,63],"精准农业","precision agriculture",[26],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"I6",[],[],[29],[71,72],"农业人工智能 全球南方 数字鸿沟 智慧农业","农业人工智能 全球南方","农业人工智能全球南方数字鸿沟智慧农业-2653","10.1080\u002F23311932.2026.2730680",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":103,"oa_url":104,"card":105,"direction":111,"ingested_from":112},"W7213346271",[78,81,84,87,90,93,96,99,101],{"name":79,"orcid":80},"Ahmed Abdiaziz Alasow","https:\u002F\u002Forcid.org\u002F0000-0002-9888-6131",{"name":82,"orcid":83},"Yusuf Hared Abdi","https:\u002F\u002Forcid.org\u002F0009-0002-7224-2247",{"name":85,"orcid":86},"Abdimalik Ali Warsame","https:\u002F\u002Forcid.org\u002F0000-0001-6130-5607",{"name":88,"orcid":89},"Yakub Burhan Abdullahi","https:\u002F\u002Forcid.org\u002F0009-0001-8535-198X",{"name":91,"orcid":92},"Mohamed Sharif Abdi","https:\u002F\u002Forcid.org\u002F0009-0007-0220-4014",{"name":94,"orcid":95},"Shazia Bashir","https:\u002F\u002Forcid.org\u002F0009-0007-6900-9870",{"name":97,"orcid":98},"Nova Ahmed","https:\u002F\u002Forcid.org\u002F0009-0009-9058-4807",{"name":100,"orcid":9},"Shuaibus Saidu Musa",{"name":102,"orcid":9},"Don Eliseo Lucero-Prisno",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F23311932.2026.2730680?needAccess=true",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"系统综述64项研究，分析AI在全球南方粮食安全中的机遇、风险与公平路径。","系统文献综述，检索Scopus、WoS、PubMed、AGRIS、IEEE Xp","AI可提升产量预测与减少浪费，但数据殖民、算法偏见等风险可能加剧不平等。","数字乡村与农业信息化","可实证检验公平导向的AI治理框架在低收入国家小农户中的落地效果与机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:17.199269Z",{"total":115,"page":21,"page_size":115,"items":116},6,[117,181,270,347,410,492],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":163,"slug":166,"view_count":103,"doi":167,"paper":168,"created_at":180},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":45,"substance":17,"depth":18,"authority":45,"freshness":13,"relevant":21,"comment":127},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[129],{"name":123,"url":120},[26,27,28,131,132],"遥感","气候分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":136},{"candidates":137,"agricultural_evidence":46},[138,143,150,154,158],{"code":32,"score":139,"supported":46,"title":140,"abstract":141,"tags":142},9,[48],[],[27],{"code":144,"score":145,"supported":46,"title":146,"abstract":147,"tags":149},"I5",3,[],[148],"农业传感",[],{"code":52,"score":21,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[28],{"code":59,"score":21,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[26],{"code":159,"score":21,"supported":14,"title":160,"abstract":161,"tags":162},"I3",[],[],[131],[164,165],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":167,"openalex_id":169,"authors":170,"venue":123,"cited_by_count":103,"oa_url":120,"card":173,"direction":179,"ingested_from":112},"W7223111766",[171],{"name":172,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":174,"method":175,"finding":176,"direction":177,"opportunity":178},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","农业遥感与作物表型","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":243,"slug":246,"view_count":145,"doi":247,"paper":248,"created_at":269},4321,"Improving early season crop type mapping using machine learning algorithms with combined SAR and optical time series","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04839-6","Timely and reliable crop type mapping is essential for sustainable agricultural management and food security. This study investigates the potential of integrating multi-temporal Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Sentinel-2 for early-season crop classification in Udham Singh Nagar district, Uttarakhand, India. Leveraging the Google Earth Engine (GEE) cloud platform, two ensemble machine learning algorithms, Random Forest (RF) and Gradient Boosted Trees (GBT), were implemented and evaluated for their ability to classify major land cover and crop types using combined SAR-optical datasets acquired during November–December 2022. A total of 1141 ground truth samples were collected through field surveys. The integration of structural (SAR) and spectral (optical) information substantially enhanced classification performance compared with single-sensor approaches. Among the models, GBT achieved the highest accuracy, with an overall accuracy of 93% and a Kappa coefficient of 0.90. Wheat, forest, and water bodies were mapped with high precision (F1 ≥ 0.90), whereas mustard and sugarcane showed lower accuracy due to spectral similarity and limited training data. Feature importance analysis identified late-season SAR (VV, VH) and optical (red-edge and SWIR) bands as the most influential variables. The results demonstrate that the synergy between SAR and optical time-series data, coupled with ensemble machine learning, provides a robust and scalable framework for near real-time crop monitoring. This approach offers valuable insights for precision agriculture and supports data-driven policy planning in India’s agricultural sector.","及时可靠的作物类型制图对可持续农业管理和粮食安全至关重要。本研究探讨了整合Sentinel-1多时相合成孔径雷达（SAR）数据与Sentinel-2光学影像在印度北阿坎德邦乌达姆辛格纳加尔地区进行早季作物分类的潜力。依托Google Earth Engine（GEE）云平台，研究采用随机森林（RF）和梯度提升树（GBT）两种集成机器学习算法，利用2022年11月至12月获取的SAR-光学联合数据集，对主要土地覆盖和作物类型进行分类，并评估其分类能力。通过实地调查共采集1141个地面真实样本。与单一传感器方法相比，结构信息（SAR）与光谱信息（光学）的融合显著提升了分类性能。在各模型中，GBT取得了最高精度，总体精度达93%，Kappa系数为0.90。小麦、森林和水体的制图精度较高（F1 ≥ 0.90），而芥菜和甘蔗因光谱相似及训练数据有限，精度较低。特征重要性分析表明，晚季SAR（VV、VH）和光学（红边和短波红外）波段是最具影响力的变量。结果表明，SAR与光学时间序列数据的协同结合集成机器学习，为近实时作物监测提供了稳健且可扩展的框架。该方法为精准农业提供了有价值的见解，并支持印度农业领域的数据驱动政策规划。","Discover Sustainability","2026-10-05T00:00:00Z",77,{"impact":191,"substance":192,"depth":18,"authority":19,"freshness":139,"relevant":21,"comment":193},16,21,"该研究将SAR与光学时序数据结合集成机器学习，实现93%早期作物分类精度，方法可扩展、结论可靠，对农业遥感监测有实质参考价值。",[195],{"name":187,"url":184},[26,27,28,131,197],"作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":201},{"candidates":202,"agricultural_evidence":46},[203,210,214,218,225,232,238],{"code":32,"score":204,"supported":46,"title":205,"abstract":207,"tags":209},15,[206],"machine learning",[208,206],"机器学习",[27],{"code":52,"score":53,"supported":46,"title":211,"abstract":212,"tags":213},[],[28,56],[28],{"code":59,"score":53,"supported":46,"title":215,"abstract":216,"tags":217},[],[62,63],[26],{"code":219,"score":115,"supported":46,"title":220,"abstract":221,"tags":224},"A2",[],[222,223],"小麦","wheat",[],{"code":226,"score":115,"supported":46,"title":227,"abstract":228,"tags":231},"A6",[],[229,230],"甘蔗","sugarcane",[],{"code":233,"score":145,"supported":46,"title":234,"abstract":235,"tags":237},"H8",[],[236],"可持续农业",[],{"code":159,"score":239,"supported":14,"title":240,"abstract":241,"tags":242},2,[],[],[197,131],[244,245],"Sentinel-1 Sentinel-2 作物分类","Google Earth Engine 机器学习 作物制图","Sentinel-1Sentinel-2作物分类-4321","10.1007\u002Fs43621-026-04839-6",{"doi":247,"openalex_id":249,"authors":250,"venue":187,"cited_by_count":103,"oa_url":184,"card":264,"direction":179,"ingested_from":112},"W7219792303",[251,254,257,259,262],{"name":252,"orcid":253},"Rajeev Ranjan Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-2670-6189",{"name":255,"orcid":256},"Jayanta Das","https:\u002F\u002Forcid.org\u002F0000-0003-0995-9114",{"name":258,"orcid":9},"Jadhav Rajaram Anna",{"name":260,"orcid":261},"Abhishek Danodia","https:\u002F\u002Forcid.org\u002F0000-0002-5724-8790",{"name":263,"orcid":9},"Dipanwita Haldar",{"tldr":265,"method":266,"finding":267,"direction":177,"opportunity":268},"融合Sentinel-1 SAR与Sentinel-2光学时序，用集成机器学习实现印度早季作物类型高","GEE平台，RF与GBT集成学习，1141个地面样本，SAR-光学多时相数据。","GBT精度最高（总体93%，Kappa 0.90），SAR与光学结合显著优于单传感器。","可探索更早物候期、小样本作物（如芥菜、甘蔗）的SAR-光学特征优化与迁移学习。","2026-10-06T23:30:38.288174Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":291,"classification":299,"search_phrases":330,"slug":333,"view_count":145,"doi":334,"paper":335,"created_at":346},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":45,"substance":18,"depth":191,"authority":115,"freshness":20,"relevant":21,"comment":280},"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。",[282],{"name":276,"url":273},[26,27,28,284,285],"供应链韧性","生产调度",{"code":52,"slug":287,"name":288,"path":289,"parent_code":290},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[292,298],{"code":293,"slug":294,"name":295,"path":296,"parent_code":297},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":300},{"candidates":301,"agricultural_evidence":46},[302,308,314,318,324],{"code":293,"score":303,"supported":46,"title":304,"abstract":306,"tags":307},11,[305],"food manufacturing",[305],[],{"code":32,"score":13,"supported":46,"title":309,"abstract":310,"tags":313},[],[311,208,48,312],"深度学习","decision support",[27],{"code":52,"score":139,"supported":46,"title":315,"abstract":316,"tags":317},[56],[],[28],{"code":59,"score":319,"supported":46,"title":320,"abstract":321,"tags":323},4,[],[322],"数字孪生",[26],{"code":325,"score":145,"supported":46,"title":326,"abstract":327,"tags":329},"E4",[],[328],"食品安全",[],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":276,"cited_by_count":103,"oa_url":340,"card":341,"direction":111,"ingested_from":112},"W7214295236",[338],{"name":339,"orcid":9},"Atif Maqbool Syed","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723457.pdf",{"tldr":342,"method":343,"finding":344,"direction":179,"opportunity":345},"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",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":9,"content":9,"source_name":352,"source_url":9,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":354,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":367,"classification":369,"search_phrases":396,"slug":399,"view_count":145,"doi":400,"paper":401,"created_at":409},2859,"县域大豆产量预测的深度学习方法:利用大规模环境数据——Frontiers in Artificial Intelligence","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1914697\u002Ffull","论文提出了基于深度学习的大规模环境数据县域大豆产量预测方法。利用县级大豆产量数据和环境变量构建预测模型,评估多种深度学习架构(卷积神经网络、循环神经网络、Transformer等)在大豆产量预测中的表现,并与传统统计回归模型进行比较。结果显示,深度学习方法在预测精度和稳定性方面优于传统模型,能够更好地捕捉环境因素与产量之间的非线性关系,为农业政策制定和粮食安全评估提供数据支持。","Frontiers in Artificial Intelligence","2026-09-17T00:00:00Z",{"impact":191,"substance":192,"depth":355,"authority":19,"freshness":13,"relevant":21,"comment":356},17,"论文以县级大规模环境数据对比多种深度学习架构与传统回归模型，方法新颖、结论可靠，对农业政策与粮食安全评估有参考价值，值得进入每日精选。",[358],{"name":352,"url":350},[26,27,28,360,361],"大豆产量预测","环境数据",{"code":363,"slug":364,"name":365,"path":366,"parent_code":290},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[368],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":370},{"candidates":371,"agricultural_evidence":46},[372,377,382,386,392],{"code":32,"score":373,"supported":46,"title":374,"abstract":375,"tags":376},20,[311,48],[311],[27],{"code":363,"score":303,"supported":46,"title":378,"abstract":380,"tags":381},[379],"大豆",[379],[],{"code":52,"score":319,"supported":46,"title":383,"abstract":384,"tags":385},[],[28],[28],{"code":387,"score":145,"supported":46,"title":388,"abstract":389,"tags":391},"J1",[],[390],"农业政策",[],{"code":59,"score":21,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[26],[397,398],"农业人工智能 大豆产量预测 智慧农业 环境数据","农业人工智能 大豆产量预测","农业人工智能大豆产量预测智慧农业环境数据-2859","10.3389\u002Ffrai.2026.1914697\u002Ffull",{"doi":400,"openalex_id":9,"authors":402,"venue":9,"cited_by_count":103,"oa_url":9,"card":403,"direction":179,"ingested_from":408},[],{"tldr":404,"method":405,"finding":406,"direction":179,"opportunity":407},"用深度学习结合大规模环境数据预测县域大豆产量，并对比多种网络架构与传统模型。","县级大豆产量与环境变量数据，采用CNN、RNN、Transformer等深度学习","深度学习在预测精度和稳定性上优于传统统计回归，能更好捕捉非线性关系。","可探索多模态环境数据融合与可解释性，提升跨区域迁移和极端气候下的预测鲁棒性。","agent","2026-09-18T00:03:31.241582Z",{"id":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":9,"source_name":416,"source_url":413,"published_at":417,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":418,"score_detail":419,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":429,"classification":431,"search_phrases":456,"slug":459,"view_count":319,"doi":460,"paper":461,"created_at":491},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment","2026-09-15T00:00:00Z",87,{"impact":17,"substance":420,"depth":18,"authority":204,"freshness":139,"relevant":21,"comment":421},23,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[423],{"name":416,"url":413},[26,27,28,425,426],"遥感监测","小麦收获",{"code":219,"slug":223,"name":222,"path":428,"parent_code":290},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[430],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":432},{"candidates":433,"agricultural_evidence":46},[434,438,443,447,451],{"code":32,"score":204,"supported":46,"title":435,"abstract":436,"tags":437},[206],[208,206],[27],{"code":219,"score":439,"supported":46,"title":440,"abstract":441,"tags":442},14,[223],[222,223],[],{"code":52,"score":53,"supported":46,"title":444,"abstract":445,"tags":446},[],[28,56],[28],{"code":59,"score":53,"supported":46,"title":448,"abstract":449,"tags":450},[],[62,63],[26],{"code":159,"score":53,"supported":46,"title":452,"abstract":453,"tags":455},[],[131,454],"remote sensing",[425],[457,458],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670","10.1016\u002Fj.rse.2026.115671",{"doi":460,"openalex_id":462,"authors":463,"venue":416,"cited_by_count":103,"oa_url":413,"card":486,"direction":177,"ingested_from":112},"W7213296259",[464,467,469,471,473,475,477,479,482,484],{"name":465,"orcid":466},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":468,"orcid":9},"Chongya Jiang",{"name":470,"orcid":9},"Jingwei An",{"name":472,"orcid":9},"Haokai Zhu",{"name":474,"orcid":9},"Yue Li",{"name":476,"orcid":9},"Xia Yao",{"name":478,"orcid":9},"Tao Cheng",{"name":480,"orcid":481},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":483,"orcid":9},"Weixing Cao",{"name":485,"orcid":9},"Yan Zhu",{"tldr":487,"method":488,"finding":489,"direction":177,"opportunity":490},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":498,"source_url":495,"published_at":499,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":500,"score_detail":501,"sources":503,"tags":505,"primary_topic":508,"secondary_topics":509,"classification":511,"search_phrases":540,"slug":543,"view_count":239,"doi":544,"paper":545,"created_at":560},1614,"Artificial intelligence adoption and food security in Saudi Arabia: the critical role of water-use efficiency management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1955058","Introduction Ensuring food security and increasing water-use efficiency have become strategic priorities for Saudi Arabia due to water scarcity, climate change, and rising food demand. Artificial intelligence (AI) advances significant opportunities to improve agricultural productivity and resource management; however, empirical evidence on its dynamic relationship with food security and water-use efficiency remains limited. Methods This study investigates the dynamic relationships among Information and Communication Technology (ICT) goods imports as an indirect proxy for AI adoption, food security, and water-use efficiency for various purposes in Saudi Arabia, utilizing annual data from 2000 to 2023. The analysis employs a Vector Autoregression (VAR) framework alongside Granger causality tests, impulse response functions (IRFs), forecast error variance decomposition (FEVD), and historical decomposition (HD). Results The results reveal that ICT goods imports, as an indirect proxy for AI adoption, significantly influence food security and industrial water-use efficiency; likewise, agricultural and service-sector water-use efficiency, in turn, promote AI adoption. The IRF results indicate that positive AI shocks improve food security and industrial water-use efficiency in the short run. However, the FEVD results show that the agricultural and service sectors will become increasingly significant factors in long-term food security. The HD outcomes support the robustness of the study results. Conclusion The study concludes that integrating AI technologies with sustainable water management can boost food security and Structural enhance resource efficiency. These outcomes provide valuable policy insights for advancing Saudi Vision 2030 through AI-driven agricultural innovation and sustainable water resource management. The study contributes to the literature by integrating ICT goods imports, as an indirect proxy for AI adoption, food security, and sector-specific water-use efficiency into a dynamic framework, providing new evidence on the potential role of digital technology diffusion in enhancing food security through improved water-use efficiency.","引言：由于水资源短缺、气候变化及粮食需求不断增长，保障粮食安全和提高用水效率已成为沙特阿拉伯的战略优先事项。人工智能（AI）的进步为提升农业生产力和资源管理提供了重大机遇；然而，关于其与粮食安全和用水效率之间动态关系的实证证据仍然有限。方法：本研究利用2000年至2023年的年度数据，探讨沙特阿拉伯信息和通信技术（ICT）产品进口（作为AI采用的间接代理指标）、粮食安全及各部门用水效率之间的动态关系。分析采用向量自回归（VAR）框架，并结合格兰杰因果关系检验、脉冲响应函数（IRFs）、预测误差方差分解（FEVD）和历史分解（HD）。结果：结果表明，ICT产品进口作为AI采用的间接代理指标，显著影响粮食安全和工业用水效率；同样，农业和服务业的用水效率反过来也促进了AI的采用。脉冲响应函数结果显示，正向的AI冲击在短期内改善了粮食安全和工业用水效率。然而，预测误差方差分解结果表明，农业和服务业将成为长期粮食安全中日益重要的因素。历史分解结果支持了研究结果的稳健性。结论：研究得出结论，将AI技术与可持续水资源管理相结合，可以提升粮食安全并增强资源效率。这些成果为通过AI驱动的农业创新和可持续水资源管理推进沙特2030愿景提供了有价值的政策启示。本研究通过将ICT产品进口（作为AI采用的间接代理指标）、粮食安全和分部门用水效率整合到一个动态框架中，为文献做出了贡献，为数字技术扩散通过改善用水效率增强粮食安全的潜在作用提供了新的证据。","Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z",76,{"impact":18,"substance":373,"depth":18,"authority":19,"freshness":53,"relevant":21,"comment":502},"研究AI采用与水资源效率对沙特粮食安全的影响，方法严谨，结论具政策参考价值。",[504],{"name":498,"url":495},[26,27,28,506,507],"水资源管理","沙特",{"code":52,"slug":287,"name":288,"path":289,"parent_code":290},[510],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":512},{"candidates":513,"agricultural_evidence":46},[514,518,522,529,536],{"code":52,"score":204,"supported":46,"title":515,"abstract":516,"tags":517},[56],[28,56],[28],{"code":32,"score":45,"supported":46,"title":519,"abstract":520,"tags":521},[48],[48],[27],{"code":523,"score":53,"supported":46,"title":524,"abstract":525,"tags":528},"H3",[],[526,527],"水资源","water resource",[506],{"code":530,"score":115,"supported":46,"title":531,"abstract":532,"tags":535},"H7",[],[533,534],"气候变化","climate change",[],{"code":59,"score":21,"supported":14,"title":537,"abstract":538,"tags":539},[],[],[26],[541,542],"农业人工智能 水资源管理 智慧农业 粮食安全","农业人工智能 水资源管理","农业人工智能水资源管理智慧农业粮食安全-1614","10.3389\u002Ffsufs.2026.1955058",{"doi":544,"openalex_id":546,"authors":547,"venue":498,"cited_by_count":103,"oa_url":554,"card":555,"direction":179,"ingested_from":112},"W7207821862",[548,550,552],{"name":549,"orcid":9},"Raga M. Elzaki",{"name":551,"orcid":9},"Omar A. Alkhateeb",{"name":553,"orcid":9},"Abdulelah A. Alasfour","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1955058\u002Fpdf",{"tldr":556,"method":557,"finding":558,"direction":179,"opportunity":559},"研究沙特AI采用与粮食安全、水资源效率的动态关系，发现AI提升短期粮食安全与工业用水效率。","使用2000-2023年数据，VAR模型、Granger因果、脉冲响应、方差分解","AI采用显著影响粮食安全与工业用水效率，农业和服务业用水效率促进AI采用，长期农业和服务业对粮食安全","可探索AI与农业用水效率的因果机制，或扩展到其他干旱地区，考虑气候变量和实际AI技术指标。","2026-09-04T23:30:08.321918Z"]