[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2050":3,"related-2050":105},{"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":83,"slug":86,"view_count":69,"doi":87,"paper":88,"created_at":104},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",{"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":45},[44,53,60,67,73,78],{"code":32,"score":18,"supported":45,"title":46,"abstract":48,"tags":52},true,[47],"artificial intelligence",[49,47,50,51],"计算机视觉","computer vision","YOLO",[28],{"code":54,"score":20,"supported":45,"title":55,"abstract":56,"tags":59},"F4",[],[57,58],"农药","pesticide",[],{"code":61,"score":62,"supported":45,"title":63,"abstract":64,"tags":66},"H3",3,[],[65],"水资源",[],{"code":68,"score":69,"supported":14,"title":70,"abstract":71,"tags":72},"I1",2,[],[],[27,30],{"code":74,"score":21,"supported":14,"title":75,"abstract":76,"tags":77},"F6",[],[],[29],{"code":79,"score":21,"supported":14,"title":80,"abstract":81,"tags":82},"I6",[],[],[26],[84,85],"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":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":94,"oa_url":95,"card":96,"direction":102,"ingested_from":103},"W7211943654",[91],{"name":92,"orcid":93},"Albina Kh. Shelepaeva","https:\u002F\u002Forcid.org\u002F0000-0002-4678-9671",0,"https:\u002F\u002Fwww.digitarin.ru\u002Fjour\u002Farticle\u002Fdownload\u002F71\u002F52",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"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",{"total":20,"page":21,"page_size":20,"items":106},[107,204,274,370,447,522],{"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":115,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":127,"primary_topic":130,"secondary_topics":131,"classification":143,"search_phrases":188,"slug":191,"view_count":21,"doi":192,"paper":193,"created_at":203},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",25,81,{"impact":17,"substance":118,"depth":18,"authority":119,"freshness":120,"relevant":21,"comment":121},20,13,8,"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[123,124],{"name":113,"url":110},{"name":125,"url":126},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,28,29,128,30,129],"农业物联网","自动驾驶农机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[132,138],{"code":133,"slug":134,"name":135,"path":136,"parent_code":137},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":139,"slug":140,"name":141,"path":142,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":144},{"candidates":145,"agricultural_evidence":45},[146,152,160,167,174,179,184],{"code":32,"score":18,"supported":45,"title":147,"abstract":148,"tags":151},[47],[149,49,150,47,50],"机器学习","machine learning",[28],{"code":133,"score":153,"supported":45,"title":154,"abstract":156,"tags":159},17,[155],"agricultural machinery",[157,158,155],"农业机械","收割机",[],{"code":139,"score":13,"supported":45,"title":161,"abstract":162,"tags":166},[],[163,164,165],"物联网","internet of things","IoT",[128],{"code":168,"score":20,"supported":45,"title":169,"abstract":170,"tags":173},"I3",[],[171,172],"遥感","remote sensing",[],{"code":68,"score":175,"supported":45,"title":176,"abstract":177,"tags":178},5,[],[30],[27,30],{"code":54,"score":62,"supported":45,"title":180,"abstract":181,"tags":183},[],[182],"除草",[],{"code":74,"score":21,"supported":14,"title":185,"abstract":186,"tags":187},[],[],[29],[189,190],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":192,"openalex_id":194,"authors":195,"venue":113,"cited_by_count":94,"oa_url":110,"card":198,"direction":102,"ingested_from":103},"W7221138004",[196],{"name":197,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":199,"method":200,"finding":201,"direction":102,"opportunity":202},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":216,"tags":218,"primary_topic":220,"secondary_topics":221,"classification":222,"search_phrases":254,"slug":257,"view_count":69,"doi":258,"paper":259,"created_at":273},4063,"An Intelligent LLM–Fuzzy Decision Support and Digital Twin Framework for Adaptive Agricultural Robotics Systems","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100423","This study develops an integrated LLM–fuzzy decision-support and digital twin framework for adaptive agricultural robotics, combining semantic reasoning, fuzzy inference, multi-objective optimization, and feedback-driven state updating. Numerical sensor data, operational information, and textual field knowledge are transformed into validated decision rules and model-ready parameters, enabling coordinated decisions across economic, operational, crop-stress, and sustainability-related objectives. The framework is evaluated as a simulation-based proof of concept calibrated with real observations from a semi-smart tomato farm, while controlled digital twin scenarios are used to assess its behavior under environmental and operational disturbances. Compared with numerical optimization alone, the complete LLM–fuzzy framework reduces operational cost by 3.35% and crop stress by 7.98%, while improving operational performance by 4.45%. Ablation, repeated-run, scenario, sensitivity, and dynamic-adaptation analyses further demonstrate consistent computational performance under the evaluated conditions. Sustainability is assessed through a model-based measure integrating water use, energy consumption, and soil-compaction-related impacts. These findings demonstrate the potential of combining semantic reasoning, uncertainty processing, optimization, and digital-twin feedback for adaptive agricultural decision support. However, the results represent a calibrated simulation-based proof of concept rather than validation through a full-scale autonomous field deployment; broader multi-season and field-scale validation is therefore required before generalizing the reported operational and sustainability benefits.","本研究开发了一个面向自适应农业机器人（adaptive agricultural robotics）的集成式LLM-模糊决策支持与数字孪生框架，融合了语义推理、模糊推理、多目标优化和反馈驱动的状态更新。数值传感器数据、运营信息和文本田间知识被转化为经过验证的决策规则和模型就绪参数，从而支持在经济、运营、作物胁迫和可持续性相关目标之间进行协调决策。该框架以基于仿真的概念验证形式进行评估，并利用半智能番茄农场的真实观测数据进行校准，同时使用受控数字孪生场景评估其在环境和运营扰动下的行为表现。与仅使用数值优化相比，完整的LLM-模糊框架将运营成本降低了3.35%，作物胁迫降低了7.98%，同时将运营性能提升了4.45%。消融实验、重复运行、场景、敏感性和动态自适应分析进一步表明，在所评估条件下该框架具有一致的计算性能。可持续性通过一个基于模型的度量进行评估，该度量综合了用水量、能源消耗和土壤压实相关影响。这些发现表明，将语义推理、不确定性处理、优化和数字孪生反馈相结合用于自适应农业决策支持具有潜力。然而，这些结果代表的是一种经过校准的基于仿真的概念验证，而非通过全尺度自主田间部署所进行的验证；因此，在将所报告的运营和可持续性效益加以推广之前，仍需开展更广泛的多季节和田间尺度验证。","AgriEngineering","2026-10-02T00:00:00Z",77,{"impact":214,"substance":17,"depth":18,"authority":119,"freshness":120,"relevant":21,"comment":215},16,"方法新颖、结论有量化支撑，但仅为仿真验证的概念验证，尚未田间落地，属细分领域前沿进展。",[217],{"name":210,"url":207},[27,28,29,219,30],"数字孪生",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":223},{"candidates":224,"agricultural_evidence":45},[225,230,235,242,249],{"code":68,"score":153,"supported":45,"title":226,"abstract":228,"tags":229},[227],"digital twin",[219,227],[219,27,30],{"code":32,"score":19,"supported":45,"title":231,"abstract":233,"tags":234},[232],"decision support",[232],[28],{"code":236,"score":20,"supported":45,"title":237,"abstract":238,"tags":241},"B1",[],[239,240],"番茄","tomato",[],{"code":243,"score":20,"supported":45,"title":244,"abstract":245,"tags":248},"H2",[],[246,247],"土壤","soil",[],{"code":74,"score":250,"supported":45,"title":251,"abstract":252,"tags":253},4,[],[29],[29],[255,256],"LLM 模糊决策 农业机器人","数字孪生 番茄农场","LLM模糊决策农业机器人-4063","10.3390\u002Fagriengineering8100423",{"doi":258,"openalex_id":260,"authors":261,"venue":210,"cited_by_count":94,"oa_url":207,"card":268,"direction":102,"ingested_from":103},"W7216447671",[262,265],{"name":263,"orcid":264},"Hamed Nozari","https:\u002F\u002Forcid.org\u002F0000-0002-6500-6708",{"name":266,"orcid":267},"Zornitsa B. Yordanova","https:\u002F\u002Forcid.org\u002F0000-0002-6056-8445",{"tldr":269,"method":270,"finding":271,"direction":100,"opportunity":272},"提出LLM-模糊决策与数字孪生框架，用于自适应农业机器人协同决策。","LLM语义推理、模糊推理、多目标优化与数字孪生仿真，基于半智能番茄农场数据。","相比纯数值优化，运营成本降3.35%、作物胁迫降7.98%、运营性能升4.45%。","可开展多季节田间规模化验证，并融合实时物联网数据提升数字孪生自适应能力。","2026-10-03T23:30:10.334001Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":277,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":282,"sources":285,"tags":287,"primary_topic":289,"secondary_topics":290,"classification":297,"search_phrases":346,"slug":349,"view_count":94,"doi":350,"paper":351,"created_at":369},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":18,"substance":17,"depth":18,"authority":119,"freshness":283,"relevant":21,"comment":284},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。",[286],{"name":280,"url":277},[27,28,163,30,288],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[291,292],{"code":139,"slug":140,"name":141,"path":142,"parent_code":36},{"code":61,"slug":293,"name":294,"path":295,"parent_code":296},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":298},{"candidates":299,"agricultural_evidence":45},[300,306,311,317,322,330,334,340],{"code":32,"score":18,"supported":45,"title":301,"abstract":302,"tags":305},[47],[303,149,49,150,304,50],"深度学习","deep learning",[28],{"code":68,"score":214,"supported":45,"title":307,"abstract":309,"tags":310},[308],"precision agriculture",[30,308],[27,30],{"code":139,"score":13,"supported":45,"title":312,"abstract":313,"tags":316},[],[163,314,165,315],"边缘计算","edge computing",[163],{"code":61,"score":283,"supported":45,"title":318,"abstract":319,"tags":321},[],[320,65,293],"灌溉",[],{"code":323,"score":324,"supported":45,"title":325,"abstract":326,"tags":329},"H7",7,[],[327,328],"气候变化","climate change",[288],{"code":243,"score":20,"supported":45,"title":331,"abstract":332,"tags":333},[],[246,247],[],{"code":335,"score":62,"supported":45,"title":336,"abstract":337,"tags":339},"A7",[],[338],"间作",[],{"code":341,"score":62,"supported":45,"title":342,"abstract":343,"tags":345},"F3",[],[344],"养分管理",[],[347,348],"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":350,"openalex_id":352,"authors":353,"venue":280,"cited_by_count":94,"oa_url":277,"card":364,"direction":102,"ingested_from":103},"W7223121456",[354,357,359,361],{"name":355,"orcid":356},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":358,"orcid":9},"Oluwadare OYEBODE",{"name":360,"orcid":9},"Iretiayo Adelaiye",{"name":362,"orcid":363},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":365,"method":366,"finding":367,"direction":100,"opportunity":368},"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":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":378,"score_detail":379,"sources":385,"tags":387,"primary_topic":389,"secondary_topics":390,"classification":391,"search_phrases":415,"slug":418,"view_count":250,"doi":419,"paper":420,"created_at":446},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","2026-10-19T00:00:00Z",93,{"impact":380,"substance":381,"depth":382,"authority":383,"freshness":283,"relevant":21,"comment":384},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。",[386],{"name":376,"url":373},[27,28,30,171,388],"全球评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":392},{"candidates":393,"agricultural_evidence":45},[394,399,403,407,411],{"code":68,"score":395,"supported":45,"title":396,"abstract":397,"tags":398},11,[],[27,30,308],[27,30],{"code":32,"score":324,"supported":45,"title":400,"abstract":401,"tags":402},[],[149,150],[28],{"code":61,"score":20,"supported":45,"title":404,"abstract":405,"tags":406},[],[320,293],[],{"code":168,"score":250,"supported":45,"title":408,"abstract":409,"tags":410},[],[171],[171],{"code":54,"score":62,"supported":45,"title":412,"abstract":413,"tags":414},[],[182],[],[416,417],"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":419,"openalex_id":421,"authors":422,"venue":376,"cited_by_count":94,"oa_url":373,"card":441,"direction":102,"ingested_from":103},"W7222974188",[423,426,429,432,435,438],{"name":424,"orcid":425},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":427,"orcid":428},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":430,"orcid":431},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":433,"orcid":434},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":436,"orcid":437},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":439,"orcid":440},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":442,"method":443,"finding":444,"direction":102,"opportunity":445},"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":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":455,"sources":458,"tags":460,"primary_topic":462,"secondary_topics":463,"classification":464,"search_phrases":479,"slug":482,"view_count":94,"doi":483,"paper":484,"created_at":521},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",{"impact":18,"substance":118,"depth":153,"authority":456,"freshness":120,"relevant":21,"comment":457},14,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[459],{"name":453,"url":450},[27,28,149,30,461],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":465},{"candidates":466,"agricultural_evidence":45},[467,471,475],{"code":32,"score":395,"supported":45,"title":468,"abstract":469,"tags":470},[],[149,150,47],[28,149],{"code":243,"score":20,"supported":45,"title":472,"abstract":473,"tags":474},[],[246,247],[],{"code":68,"score":69,"supported":14,"title":476,"abstract":477,"tags":478},[],[],[27,30],[480,481],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":483,"openalex_id":485,"authors":486,"venue":453,"cited_by_count":94,"oa_url":450,"card":516,"direction":100,"ingested_from":103},"W7222866367",[487,490,493,496,499,502,505,508,510,513],{"name":488,"orcid":489},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":491,"orcid":492},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":494,"orcid":495},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":497,"orcid":498},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":500,"orcid":501},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":503,"orcid":504},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":506,"orcid":507},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":509,"orcid":9},"M. Z. Walkup",{"name":511,"orcid":512},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":514,"orcid":515},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":517,"method":518,"finding":519,"direction":100,"opportunity":520},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":523,"title":524,"url":525,"summary":526,"summary_zh":527,"content":9,"source_name":528,"source_url":525,"published_at":529,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":530,"score_detail":531,"sources":534,"tags":536,"primary_topic":539,"secondary_topics":540,"classification":541,"search_phrases":552,"slug":555,"view_count":94,"doi":556,"paper":557,"created_at":587},5153,"Real-Time UAV scheduling under hazardous weather conditions using an integrated fuzzy logic and machine learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00489-2","The use of Unmanned Aerial Vehicles (UAVs) in precision agriculture, specifically palm farm management, is perpetually challenged by dynamic and extreme weather. From sudden wind gusts and thermal stress to destructive regional events like sandstorms and flash floods, these hazards severely constrict operational windows. This paper proposes a robust, hybrid scheduling framework that integrates fuzzy logic inference with an ensemble of machine learning classifiers to enhance real-time UAV flight reliability. The primary novelty of this research lies not in a new mathematical paradigm, but in its specialized application: the holistic integration of region-specific agricultural hazards into a real-time, decision-fusion architecture. The framework classifies takeoff viability into three categories (SAFE, CAUTION, and UNSAFE) automatically issuing alerts to trigger preventative measures and preserve hardware. By leveraging both short-term farm datasets and a decade of historical weather records, the fuzzy logic component reacts to rapid environmental shifts while establishing safety baselines. Simultaneously, tree-based ensemble models (Gradient Boosting, XGBoost, LightGBM, and CatBoost) analyze non-linear atmospheric interactions, achieving up to 99.67% accuracy in predicting safe windows. Comprehensive sensitivity analyses, including evaluations without SMOTE and seasonal temporal holdout validations, confirm the model’s robustness against class imbalance and unseen extremes. Empirical benchmarking demonstrates the framework's superiority over existing hybrid approaches, highlighting the necessity of combining adaptive fuzzy reasoning with deep historical learning for smart farming.","无人机（UAV）在精准农业尤其是棕榈农场管理中的应用，始终受到动态极端天气的困扰。从突发阵风和热应激到沙尘暴和山洪等破坏性区域事件，这些灾害严重压缩了作业窗口。本文提出了一种稳健的混合调度框架，将模糊逻辑推理与机器学习分类器集成相结合，以提升无人机实时飞行的可靠性。本研究的主要创新点不在于新的数学范式，而在于其专门化应用：将区域特定的农业灾害整体集成到实时决策融合架构中。该框架将起飞可行性分为三类（安全、 caution、不安全），自动发出警报以触发预防措施并保护硬件。通过利用短期农场数据集和十年历史天气记录，模糊逻辑组件能够对快速环境变化作出反应，同时建立安全基线。与此同时，基于树的集成模型（梯度提升、XGBoost、LightGBM和CatBoost）分析非线性大气相互作用，在预测安全窗口方面达到高达99.67%的准确率。全面的敏感性分析，包括无SMOTE的评估和季节性时间留出验证，证实了模型对类别不平衡和未见极端情况的稳健性。实证基准测试表明，该框架优于现有的混合方法，凸显了将自适应模糊推理与深度历史学习相结合对于智慧农业的必要性。","Discover Internet of Things","2026-10-15T00:00:00Z",76,{"impact":214,"substance":532,"depth":18,"authority":119,"freshness":120,"relevant":21,"comment":533},21,"将模糊逻辑与集成学习融合用于极端天气下的农业无人机实时调度，方法扎实、验证充分，对智慧农业作业安全有实际参考价值。",[535],{"name":528,"url":525},[27,28,537,30,538],"农业无人机","气象预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":542},{"candidates":543,"agricultural_evidence":45},[544,548],{"code":32,"score":383,"supported":45,"title":545,"abstract":546,"tags":547},[150],[149,150],[28],{"code":68,"score":395,"supported":45,"title":549,"abstract":550,"tags":551},[],[27,30,308],[27,30],[553,554],"无人机 精准农业 极端天气","UAV 棕榈园 调度","无人机精准农业极端天气-5153","10.1007\u002Fs43926-026-00489-2",{"doi":556,"openalex_id":558,"authors":559,"venue":528,"cited_by_count":94,"oa_url":581,"card":582,"direction":102,"ingested_from":103},"W7166891579",[560,563,566,568,570,573,575,578],{"name":561,"orcid":562},"Ahmad B. Alkhodre","https:\u002F\u002Forcid.org\u002F0000-0001-6168-3552",{"name":564,"orcid":565},"Adnan Ahmed Abi Sen","https:\u002F\u002Forcid.org\u002F0000-0003-2960-5807",{"name":567,"orcid":9},"Abdelouahab Abid",{"name":569,"orcid":9},"Nour Mahmoud Bahbouh",{"name":571,"orcid":572},"Adel Ben Mnaouer","https:\u002F\u002Forcid.org\u002F0000-0003-3617-7636",{"name":574,"orcid":9},"Mohamed Benaida",{"name":576,"orcid":577},"Yazed Alsaawy","https:\u002F\u002Forcid.org\u002F0000-0001-5031-3388",{"name":579,"orcid":580},"Hani Almoamari","https:\u002F\u002Forcid.org\u002F0000-0002-3550-8977","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43926-026-00489-2.pdf",{"tldr":583,"method":584,"finding":585,"direction":102,"opportunity":586},"提出融合模糊逻辑与机器学习分类器的无人机实时调度框架，在恶劣天气下保障精准农业飞行安全。","模糊逻辑推理结合梯度提升、XGBoost、LightGBM、CatBoost集成","框架将起飞可行性分为安全、警告、不安全三级，预测安全窗口准确率最高达99.67%，优于现有混合方法。","可将区域特定灾害（沙尘暴、山洪）纳入实时决策融合，并扩展到多机协同调度与边缘端轻量化部署研究。","2026-10-17T23:30:08.889571Z"]