[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3768":3,"related-3768":106},{"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":28,"primary_topic":9,"secondary_topics":34,"classification":35,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":105},3768,"Ai-Driven Agricultural Practices And Their Contribution To Rural Economic Growth","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23009415","Abstract Data combining field observations with information from farm sensors, remote sensing, weather services, agricultural machinery, mobile apps, and e-commerce are increasingly underpinning new, intelligent ways of supporting agriculture decisions. This paper reviews the evidence for improvements in farm productivity and efficiency that result from these AI-supported farming practices and discusses how these translate to broader rural economic growth. Employing structured secondary research including peer-reviewed literature, international development agency reports and recent work on digital agriculture and AI, the review explores how the following five major channels are affected: precise crop management; smart irrigation; crop and yield forecasts; pest and disease diagnostics; and data-enabled farm and market decisions. This evidence shows how AI can help enable a better timing of, and a greater precision in the delivery of, agricultural inputs, reducing uneconomic resource use, improving risk management and strengthening farmer access to information and markets. While yield improvements and cost savings, which are frequently reported in the literature, range widely in relation to crop, technology, location, farm size and implementation, we would characterize yield improvements from 12-45% and cost savings up to around 25%, as cited in the initial framework, to be illustrative indications from the literature. Indeed, this paper reveals how the economic benefits of AI extend beyond the level of the farm itself – a better ability to utilize local technical services, advice, equipment maintenance, data services, transportation services, aggregation of product, and other activities throughout rural value chains. These can all potentially be spurring by well-placed AI support. The high initial outlay needed to use the technology, the lack of good connections, the small size of many plots of land, the digital literacy constraints of farmers and local intermediaries, concerns with how data should be managed and the need for access to respected local sources of expertise all constrain widespread adoption. Given these considerations, an approach to implementing AI as a support system rather than a replacement, which focuses on the farmer, includes public-good infrastructure and training, access to cost effective service models, tools designed to suit local circumstances and language, and the correct partnerships, is most apt to unlock inclusive rural economic development.","摘要 将田间观测与来自农场传感器、遥感、气象服务、农业机械、移动应用程序和电子商务的信息相结合的数据，正日益支撑着支持农业决策的新型智能方式。本文综述了这些人工智能支持的农业实践所带来的农场生产力和效率提升的证据，并讨论了这些提升如何转化为更广泛的农村经济增长。通过采用结构化二次研究，包括同行评审文献、国际发展机构报告以及近期关于数字农业和人工智能的研究，本综述探讨了以下五个主要渠道如何受到影响：精准作物管理；智能灌溉；作物与产量预测；病虫害诊断；以及数据驱动的农场与市场决策。这些证据表明，人工智能如何有助于实现农业投入品更佳的时机选择和更高的精准度，减少不经济的资源使用，改善风险管理，并加强农民获取信息和进入市场的能力。虽然文献中经常报告的产量提升和成本节约因作物、技术、地点、农场规模和实施方式的不同而差异很大，但我们将初始框架中引用的12%至45%的产量提升和最高约25%的成本节约，视为文献中具有说明性的指示。事实上，本文揭示了人工智能的经济效益如何超越农场本身——更好地利用当地技术服务、咨询、设备维护、数据服务、运输服务、产品集散以及农村价值链中的其他活动。这些都可能因恰当的人工智能支持而得到促进。使用该技术所需的高昂初始投入、缺乏良好连接、许多地块面积小、农民和当地中介的数字素养限制、对数据应如何管理的担忧，以及需要获得受尊重的当地专业知识的来源，都制约着广泛采用。鉴于这些考虑，将人工智能作为一种支持系统而非替代品来实施，以农民为中心，包括公共产品基础设施和培训、获得具有成本效益的服务模式、适合当地情况和语言设计的工具，以及正确的伙伴关系，最有可能释放包容性的农村经济",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,16,13,10,1,"系统性综述AI驱动农业实践对农村经济增长的贡献，含产量提升12-45%、成本节约约25%等量化结论，但属文献综述而非原创突破，适合主题聚合。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23009416",[29,30,31,32,33],"数字乡村","智慧农业","农业人工智能","精准农业","农村经济",[],{"status":36,"locked":14,"version":37,"method":38,"evidence":39},"needs_review","2026-10-22.2","rules",{"candidates":40,"agricultural_evidence":44,"reason":82},[41,50,58,65,72,77],{"code":42,"score":43,"supported":44,"title":45,"abstract":46,"tags":49},"I1",8,true,[],[47,48],"数字农业","digital agriculture",[30,32],{"code":51,"score":52,"supported":44,"title":53,"abstract":54,"tags":57},"F5",6,[],[55,56],"农业机械","agricultural machinery",[],{"code":59,"score":52,"supported":44,"title":60,"abstract":61,"tags":64},"H3",[],[62,63],"灌溉","irrigation",[],{"code":66,"score":52,"supported":44,"title":67,"abstract":68,"tags":71},"I3",[],[69,70],"遥感","remote sensing",[],{"code":73,"score":22,"supported":14,"title":74,"abstract":75,"tags":76},"I2",[],[],[31],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"I6",[],[],[29],"多个产业证据相当，需要确认主归属",[84,85],"AI 农村经济增长","数字农业 精准灌溉 病虫害诊断","AI农村经济增长-3768",3,"10.5281\u002Fzenodo.23009415",{"doi":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":96,"oa_url":6,"card":97,"direction":103,"ingested_from":104},"W7214577610",[92,94],{"name":93,"orcid":9},"Sharad Shivaji Salve",{"name":95,"orcid":9},"Santosh Prakash Dhawale",0,{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"综述AI驱动农业实践通过五大渠道提升生产力并促进农村经济增长。","结构化二次研究，综合同行文献、国际机构报告与数字农业AI研究。","AI可提升产量12-45%、节省成本约25%，并带动农村价值链发展，但受成本、连接和数字素养制约。","农业人工智能与决策模型","可研究小农户低成本AI服务模式、本地化工具与数据治理，以促进包容性农村经济增长。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:08.739511Z",{"total":52,"page":22,"page_size":52,"items":107},[108,187,292,347,390,467],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":21,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":125,"secondary_topics":130,"classification":131,"search_phrases":169,"slug":172,"view_count":156,"doi":173,"paper":174,"created_at":186},2050,"The use of artificial intelligence in agriculture: Global practice","https:\u002F\u002Fdoi.org\u002F10.26794\u002F3030-7097-2026-2-3-16-25","This article is devoted to the analysis of current trends and practices of the application of artificial intelligence (AI) in agriculture based on peer-reviewed scientific sources of 2022–2026. The purpose of the work is a comprehensive study of the problems and key trends of the development of AI in the agricultural sector, as well as the systematization of quantitative data confirming the effectiveness of the implemented technologies. The article discusses the main areas of AI use: crop yield forecasting based on neural network algorithms (LSTM, CNN), plant health monitoring and precise resource application using computer vision (ResNet, YOLO), robotization of production processes, and the introduction of intelligent systems into the economy and management of the agro-industrial complex. Based on the analysis of empirical studies, the article systematizes performance indicators: the accuracy of plant disease diagnosis reaches 99.2%, the reduction of pesticide use is up to 90%, water resources are saved by up to 46%, and farm income increases by 15–20%. Special attention is paid to a comparative analysis of the barriers to the implementation of AI in different groups of countries. In developed countries, the main problems are technological fragmentation and a shortage of qualified personnel. In BRICS countries, there are infrastructure limitations and a gap between science and production. In developing countries, the key obstacles are the lack of basic infrastructure, the low solvency of small farms, and a lack of digital literacy. The final part identifies understudied aspects that require further research, such as socio-psychological barriers to technology adoption, the environmental impact of AI solutions, issues related to data sovereignty and monetization, economic efficiency for small-scale farmers, cybersecurity, and ethical dilemmas in breeding.","本文基于2022—2026年经同行评审的科学文献，分析人工智能（AI）在农业中应用的当前趋势与实践。研究目的是全面探讨AI在农业领域发展中的问题与主要趋势，并系统整理证实所实施技术有效性的定量数据。文章讨论了AI应用的主要方向：基于神经网络算法（LSTM、CNN）的作物产量预测，利用计算机视觉（ResNet、YOLO）进行植物健康监测与精准资源施用，生产过程的机器人化，以及智能系统在农工综合体的经济与管理中的引入。基于对实证研究的分析，文章系统整理了绩效指标：植物病害诊断准确率高达99.2%，农药使用量减少达90%，水资源节约达46%，农场收入增加15—20%。文章特别关注对不同国家组别中AI实施障碍的比较分析。在发达国家，主要问题是技术碎片化和合格人员短缺。在金砖国家，存在基础设施限制以及科学与生产之间的脱节。在发展中国家，关键障碍是缺乏基础基础设施、小农场支付能力低以及数字素养不足。最后一部分指出了需要进一步研究的薄弱环节，如技术采用的社会心理障碍、AI解决方案的环境影响、数据主权与货币化相关问题、小规模农户的经济效率、网络安全以及育种中的伦理困境。","Digital Solutions and Artificial Intelligence Technologies","2026-09-07T00:00:00Z",80,{"impact":118,"substance":118,"depth":17,"authority":119,"freshness":52,"relevant":22,"comment":120},22,12,"基于2022—2026年同行评审文献系统梳理AI在农业的应用成效与国别障碍，数据翔实、结论可靠，对智慧农业研究与政策制定有较高参考价值。",[122],{"name":114,"url":111},[29,30,31,124,32],"农业机器人",{"code":73,"slug":126,"name":127,"path":128,"parent_code":129},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":132,"locked":14,"version":37,"method":38,"evidence":133},"classified",{"candidates":134,"agricultural_evidence":44},[135,143,150,155,160,165],{"code":73,"score":17,"supported":44,"title":136,"abstract":138,"tags":142},[137],"artificial intelligence",[139,137,140,141],"计算机视觉","computer vision","YOLO",[31],{"code":144,"score":52,"supported":44,"title":145,"abstract":146,"tags":149},"F4",[],[147,148],"农药","pesticide",[],{"code":59,"score":87,"supported":44,"title":151,"abstract":152,"tags":154},[],[153],"水资源",[],{"code":42,"score":156,"supported":14,"title":157,"abstract":158,"tags":159},2,[],[],[30,32],{"code":161,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},"F6",[],[],[124],{"code":78,"score":22,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[29],[170,171],"农业人工智能 农业机器人 数字乡村 智慧农业","农业人工智能 农业机器人","农业人工智能农业机器人数字乡村智慧农业-2050","10.26794\u002F3030-7097-2026-2-3-16-25",{"doi":173,"openalex_id":175,"authors":176,"venue":114,"cited_by_count":96,"oa_url":180,"card":181,"direction":103,"ingested_from":104},"W7211943654",[177],{"name":178,"orcid":179},"Albina Kh. Shelepaeva","https:\u002F\u002Forcid.org\u002F0000-0002-4678-9671","https:\u002F\u002Fwww.digitarin.ru\u002Fjour\u002Farticle\u002Fdownload\u002F71\u002F52",{"tldr":182,"method":183,"finding":184,"direction":101,"opportunity":185},"综述2022-2026年AI在农业的应用趋势、成效与各国推广障碍。","基于同行评议文献综述，分析LSTM、CNN、ResNet、YOLO等AI技术应用","AI使病害诊断准确率达99.2%，农药减90%、节水46%、收入增15-20%，但各国障碍不同。","可研究小农户AI采纳的社会心理障碍、数据主权与AI环境影响的量化评估。","2026-09-10T23:30:16.002269Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":21,"is_selected":14,"score":116,"score_detail":195,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":204,"classification":214,"search_phrases":268,"slug":271,"view_count":96,"doi":272,"paper":273,"created_at":291},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",{"impact":17,"substance":118,"depth":17,"authority":20,"freshness":196,"relevant":22,"comment":197},9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[199],{"name":193,"url":190},[30,31,201,32,202],"物联网","气候韧性",{"code":73,"slug":126,"name":127,"path":128,"parent_code":129},[205,210],{"code":206,"slug":207,"name":208,"path":209,"parent_code":129},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":59,"slug":63,"name":211,"path":212,"parent_code":213},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":132,"locked":14,"version":37,"method":38,"evidence":215},{"candidates":216,"agricultural_evidence":44},[217,225,230,237,241,249,256,262],{"code":73,"score":17,"supported":44,"title":218,"abstract":219,"tags":224},[137],[220,221,139,222,223,140],"深度学习","机器学习","machine learning","deep learning",[31],{"code":42,"score":19,"supported":44,"title":226,"abstract":228,"tags":229},[227],"precision agriculture",[32,227],[30,32],{"code":206,"score":21,"supported":44,"title":231,"abstract":232,"tags":236},[],[201,233,234,235],"边缘计算","IoT","edge computing",[201],{"code":59,"score":196,"supported":44,"title":238,"abstract":239,"tags":240},[],[62,153,63],[],{"code":242,"score":243,"supported":44,"title":244,"abstract":245,"tags":248},"H7",7,[],[246,247],"气候变化","climate change",[202],{"code":250,"score":52,"supported":44,"title":251,"abstract":252,"tags":255},"H2",[],[253,254],"土壤","soil",[],{"code":257,"score":87,"supported":44,"title":258,"abstract":259,"tags":261},"A7",[],[260],"间作",[],{"code":263,"score":87,"supported":44,"title":264,"abstract":265,"tags":267},"F3",[],[266],"养分管理",[],[269,270],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":272,"openalex_id":274,"authors":275,"venue":193,"cited_by_count":96,"oa_url":190,"card":286,"direction":103,"ingested_from":104},"W7223121456",[276,279,281,283],{"name":277,"orcid":278},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":280,"orcid":9},"Oluwadare OYEBODE",{"name":282,"orcid":9},"Iretiayo Adelaiye",{"name":284,"orcid":285},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":287,"method":288,"finding":289,"direction":101,"opportunity":290},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":9,"content":9,"source_name":297,"source_url":295,"published_at":298,"category":12,"cover_url":9,"hotness":21,"is_selected":14,"score":299,"score_detail":300,"sources":304,"tags":306,"primary_topic":309,"secondary_topics":313,"classification":314,"search_phrases":326,"slug":329,"view_count":96,"doi":330,"paper":331,"created_at":346},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":19,"substance":301,"depth":17,"authority":302,"freshness":21,"relevant":22,"comment":303},21,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[305],{"name":297,"url":295},[30,31,32,307,308],"机器视觉","作物行检测",{"code":42,"slug":310,"name":311,"path":312,"parent_code":129},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":132,"locked":14,"version":37,"method":38,"evidence":315},{"candidates":316,"agricultural_evidence":44},[317,322],{"code":42,"score":318,"supported":44,"title":319,"abstract":320,"tags":321},5,[],[227],[30,32],{"code":73,"score":156,"supported":14,"title":323,"abstract":324,"tags":325},[],[],[31,307],[327,328],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":330,"openalex_id":332,"authors":333,"venue":297,"cited_by_count":96,"oa_url":9,"card":9,"direction":9,"ingested_from":104},"W7223117828",[334,337,340,343],{"name":335,"orcid":336},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":338,"orcid":339},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":341,"orcid":342},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":344,"orcid":345},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":9,"content":352,"source_name":353,"source_url":9,"published_at":354,"category":355,"cover_url":9,"hotness":21,"is_selected":14,"score":356,"score_detail":357,"sources":359,"tags":361,"primary_topic":364,"secondary_topics":368,"classification":369,"search_phrases":385,"slug":388,"view_count":96,"doi":9,"paper":9,"created_at":389},5505,"广西以赛聚智赋能「AI+农业」 桂字号品牌与东盟水果IP赛道决出","https:\u002F\u002Fwww.agri.cn\u002Fzt\u002Fzhzz\u002Ftp\u002F202610\u002Ft20261019_8877223.htm","10月16日2026年广西智慧农业（数字乡村）创新大赛「桂字号」农业品牌推广赛道、中国（广西）—东盟水果贸易第一IP打造赛道决赛举行，55个项目同台竞技。自治区农业农村厅提出广西正按「北上广研发+广西集成+东盟应用」路径打造中国（广西）—东盟智慧农业先行区，去年一批项目已入选广西人工智能赋能千行百业拟补贴名单。","自治区农业农村厅党组成员、副厅长欧毅霞表示，本次大赛是落实中央一号文件关于因地制宜发展农业新质生产力、大力发展智慧农业部署的具体行动，也是推动人工智能与农业深度融合的重要举措。今年大赛聚焦“桂字号”农业品牌推广和中国（广西）—东盟水果贸易第一IP打造这两个赛道，主要基于三点考虑：一是广西农业资源丰富，“桂字号”品牌基础扎实，但数字化运营和品牌传播仍是短板，需要以赛聚智、以赛促创，借助数字化手段让好产品卖出好价钱；二是广西面向东盟的区位优势得天独厚，水果贸易体量大、潜力足，亟需打造具有国际影响力的第一IP，以数字技术赋能贸易全链条，提升通关、溯源、营销等环节效率；三是这两个赛道切口小、场景实、应用性强，能够有效打通“技术—产业—市场”闭环，推动创新链与产业链精准对接，让更多“金种子”项目在八桂大地上落地生根、开花结果。\n\n欧毅霞提出三点希望：专业组勇当产业升级“领跑者”，形成可复制、可推广的解决方案；学生组争做乡村振兴“生力军”，把创新火花带回课堂、实验室和田间地头；各方共筑智慧农业“生态圈”，推动技术、资本、人才、政策等要素集聚。\n\n广西农业职业技术大学党委书记韦波在致辞中表示，作为承办方，学校将联合广西数字乡村研究院，在赛道组委会统一领导下做好组织、服务与保障，推动赛事成果与教育教学、产业需求有效对接，发挥面向东盟农业职业教育合作优势，为“桂字号”出海、中国—东盟水果贸易双向流通搭建桥梁。\n\n据介绍，广西正按照“北上广研发+广西集成+东盟应用”路径，打造中国（广西）—东盟智慧农业先行区。智慧农业创新大赛不仅是竞技舞台，更是创新成果转化、产业赋能的重要平台。去年，广西首次举办智慧农业创新大赛并取得丰硕成果，一批新技术、新理念、新模式脱颖而出，逐步被大家看见、被市场认可；如捷佳润科技跨境农业智能决策系统、北部湾产权交易所集团AI赋能广西桑蚕茧丝绸全产业链项目入选广西人工智能赋能千行百业拟补贴项目名单；荣获“最佳潜力奖”的“桂鸟先飞—滴滴打药平台”获得地方政府投资扩容实现了升级蝶变。\n\n以赛为媒，向新而行。随着多个项目在决赛中展开比拼，一批聚焦品牌数字化运营、跨境贸易、品控溯源和IP打造的创新项目将在八桂大地加速落地。未来，广西将持续以大赛为纽带，推动技术、资本、人才、政策等要素向智慧农业集聚，促进创新链、产业链、资金链、人才链深度融合，让更多“金种子”项目生根发芽、茁壮成长，为“桂字号”香飘东盟、中国—东盟水果贸易双向流通注入新动能，助力中国（广西）—东盟智慧农业先行区建设迈上新台阶。","中国农业农村信息网 2026-10-19","2026-10-19T00:00:00Z","报道",66,{"impact":17,"substance":19,"depth":302,"authority":119,"freshness":52,"relevant":22,"comment":358},"省级智慧农业赛事报道，聚焦AI赋能品牌与东盟水果贸易，有具体路径与落地案例，但属活动通稿，时效稍滞后。",[360],{"name":353,"url":350},[29,30,31,362,363],"桂字号","东盟水果贸易",{"code":78,"slug":365,"name":366,"path":367,"parent_code":129},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":132,"locked":14,"version":37,"method":38,"evidence":370},{"candidates":371,"agricultural_evidence":44},[372,377,381],{"code":42,"score":373,"supported":44,"title":374,"abstract":375,"tags":376},4,[],[30],[30],{"code":78,"score":373,"supported":44,"title":378,"abstract":379,"tags":380},[],[29],[29],{"code":73,"score":22,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[31],[386,387],"广西 智慧农业创新大赛","桂字号 东盟水果 IP","广西智慧农业创新大赛-5505","2026-10-22T00:06:58.572242Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":354,"category":12,"cover_url":9,"hotness":21,"is_selected":44,"score":397,"score_detail":398,"sources":404,"tags":406,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":435,"slug":438,"view_count":373,"doi":439,"paper":440,"created_at":466},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences",93,{"impact":399,"substance":400,"depth":401,"authority":402,"freshness":196,"relevant":22,"comment":403},26,24,19,15,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[405],{"name":396,"url":393},[30,31,32,69,407],"全球评估",{"code":73,"slug":126,"name":127,"path":128,"parent_code":129},[],{"status":132,"locked":14,"version":37,"method":38,"evidence":411},{"candidates":412,"agricultural_evidence":44},[413,418,422,426,430],{"code":42,"score":414,"supported":44,"title":415,"abstract":416,"tags":417},11,[],[30,32,227],[30,32],{"code":73,"score":243,"supported":44,"title":419,"abstract":420,"tags":421},[],[221,222],[31],{"code":59,"score":52,"supported":44,"title":423,"abstract":424,"tags":425},[],[62,63],[],{"code":66,"score":373,"supported":44,"title":427,"abstract":428,"tags":429},[],[69],[69],{"code":144,"score":87,"supported":44,"title":431,"abstract":432,"tags":434},[],[433],"除草",[],[436,437],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":439,"openalex_id":441,"authors":442,"venue":396,"cited_by_count":96,"oa_url":393,"card":461,"direction":103,"ingested_from":104},"W7222974188",[443,446,449,452,455,458],{"name":444,"orcid":445},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":447,"orcid":448},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":450,"orcid":451},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":453,"orcid":454},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":456,"orcid":457},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":459,"orcid":460},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":462,"method":463,"finding":464,"direction":103,"opportunity":465},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":9,"content":9,"source_name":472,"source_url":9,"published_at":473,"category":355,"cover_url":9,"hotness":21,"is_selected":14,"score":474,"score_detail":475,"sources":477,"tags":479,"primary_topic":482,"secondary_topics":483,"classification":484,"search_phrases":510,"slug":513,"view_count":96,"doi":9,"paper":9,"created_at":514},5334,"让 AI 赋能民族乡高质量发展:从经验农业到数据农业的关键一跃","https:\u002F\u002Fbig5.china.com.cn\u002Fgate\u002Fbig5\u002Fszjj.china.com.cn\u002F2026-10\u002F14\u002Fcontent_43497785.html","评论指出 AI 应成为民族乡田野上的新农具。贵州雷山用 AI 导游+云端茶园盘活苗岭资源,广西崇左以 AI 重构甘蔗糖业链条;民族乡多语言、多习俗、山寨分散,治理成本高,数字乡村高质量发展行动计划(2026—2030 年)明确提出远程医疗、智慧政务、数字健康向边远地区倾斜。同时强调要防止技术悬浮(设备闲置、群众不会用)和文化失语(算法把民族故事包装成流量模板)。AI 须懂方言、界面要适老、数据权属要清晰,非遗内容要人工复核。","中国网 2026-10-14","2026-10-14T00:00:00Z",64,{"impact":17,"substance":19,"depth":402,"authority":119,"freshness":87,"relevant":22,"comment":476},"央媒评论结合贵州雷山、广西崇左案例与数字乡村行动计划，观点有实质，但属评论性报道且已发布近一周，时效偏弱，可作主题聚合而非每日精选。",[478],{"name":472,"url":470},[29,30,31,480,481],"数字治理","民族地区",{"code":78,"slug":365,"name":366,"path":367,"parent_code":129},[],{"status":132,"locked":14,"version":37,"method":38,"evidence":485},{"candidates":486,"agricultural_evidence":44},[487,491,497,502,506],{"code":78,"score":373,"supported":44,"title":488,"abstract":489,"tags":490},[],[29],[29],{"code":492,"score":87,"supported":44,"title":493,"abstract":494,"tags":496},"A6",[],[495],"甘蔗",[],{"code":498,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},"G5",[],[],[480],{"code":42,"score":22,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[30],{"code":73,"score":22,"supported":14,"title":507,"abstract":508,"tags":509},[],[],[31],[511,512],"雷山 云端茶园 AI导游","崇左 甘蔗糖业 AI","雷山云端茶园AI导游-5334","2026-10-20T02:47:36.879141Z"]