[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2050":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":48},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。",null,"Digital Solutions and Artificial Intelligence Technologies","2026-09-07T00:00:00Z","论文",10,false,80,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,12,6,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业人工智能","农业机器人","精准农业",0,"10.26794\u002F3030-7097-2026-2-3-16-25",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":39,"card":40,"direction":46,"ingested_from":47},"W7211943654",[36],{"name":37,"orcid":38},"Albina Kh. Shelepaeva","https:\u002F\u002Forcid.org\u002F0000-0002-4678-9671","https:\u002F\u002Fwww.digitarin.ru\u002Fjour\u002Farticle\u002Fdownload\u002F71\u002F52",{"tldr":41,"method":42,"finding":43,"direction":44,"opportunity":45},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:16.002269Z"]