[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1711":3,"related-1711":93},{"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":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":92},1711,"Path planning for unmanned combined harvester edging operation based on UAV imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112382","Path planning for unmanned combined harvester edging operation based on UAV imagery。Computers and Electronics in Agriculture","基于无人机影像的无人联合收割机边缘作业路径规划。《计算机与电子在农业中的应用》",null,"Computers and Electronics in 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surfaces.Median filtering is used to reduce the influence of anomalous ultrasonic measurements, while the measured distances are converted into geometric parameters using the known dimensions of the experimental installation.The obtained parameters are used by the controller to define the permissible operating area and generate control actions for the washing carriage, pump, and air-blowing system.If the machinery is positioned incorrectly or exceeds the permissible dimensions of the working zone, the technological cycle is blocked.Experimental verification using the developed automated washing installation and a tractor model confirmed correct operation of the positioning subsystem and sufficient accuracy of ultrasonic measurements.The maximum absolute error did not exceed 2 mm.The proposed approach provides direct integration of sensor measurements into the control logic and enables adaptation of the washing cycle to the actual position and dimensions of the machinery.Further research should address the integration of computer vision and adaptive generation of washing-carriage trajectories.","本文提出并实验验证了一种用于自动化清洗过程控制的农业机械位置与几何参数确定方法。该方法结合光电传感器验证机械的允许位置，并利用超声波传感器确定其顶面和侧面距离。采用中值滤波降低异常超声测量的影响，同时利用实验装置的已知尺寸将测量距离转换为几何参数。控制器利用所得参数确定允许作业区域，并生成对清洗小车、水泵和吹风系统的控制动作。若机械位置不正确或超出工作区允许尺寸，则封锁工艺循环。利用所开发的自动化清洗装置和拖拉机模型进行的实验验证，证实了定位子系统的正确运行以及超声测量的足够精度。最大绝对误差不超过2 mm。所提方法实现了传感器测量与控制逻辑的直接集成，并使清洗循环能够适应机械的实际位置和尺寸。后续研究应关注计算机视觉的集成以及清洗小车轨迹的自适应生成。","Ìnformatika, kulʹtura, tehnìka.","2026-10-13T00:00:00Z",{"impact":205,"substance":206,"depth":19,"authority":13,"freshness":108,"relevant":21,"comment":207},12,20,"该论文提出光电与超声传感器融合的农机自动清洗定位方法，实验误差不超过2毫米，方法新颖且数据可靠，但属细分技术进展，公共影响有限。",[209],{"name":202,"url":199},[26,30,211,212,213],"农业传感器","自动控制","超声测距",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":217},{"candidates":218,"agricultural_evidence":45},[219,225,231,235],{"code":32,"score":13,"supported":45,"title":220,"abstract":221,"tags":224},[],[125,222,223],"agricultural machinery","tractor",[30],{"code":57,"score":94,"supported":45,"title":226,"abstract":227,"tags":230},[],[228,229],"计算机视觉","computer vision",[],{"code":52,"score":21,"supported":14,"title":232,"abstract":233,"tags":234},[],[],[26],{"code":236,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},"I5",[],[],[211],[241,242],"农业机械 自动清洗 定位","超声波传感器 农机 几何参数","农业机械自动清洗定位-4995","10.15276\u002Fict.03.2026.76",{"doi":244,"openalex_id":246,"authors":247,"venue":202,"cited_by_count":84,"oa_url":248,"card":249,"direction":89,"ingested_from":91},"W7221711885",[],"https:\u002F\u002Fict.od.ua\u002Findex.php\u002Fjournal\u002Fuk\u002Farticle\u002Fdownload\u002F186\u002F185\u002F620",{"tldr":250,"method":251,"finding":252,"direction":89,"opportunity":253},"开发并实验验证一种基于光电与超声传感器确定农机位置和几何参数以自动控制清洗过程的方法。","光电传感器验证位置，超声传感器测距，中值滤波降噪，转换为几何参数供控制器使用。","定位子系统运行正确，超声测量最大绝对误差不超过2毫米，可自适应调整清洗周期。","可融合计算机视觉与自适应轨迹生成，提升农机清洗的智能化与柔性化水平。","2026-10-15T23:30:11.641686Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":260,"source_name":261,"source_url":9,"published_at":262,"category":102,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":271,"secondary_topics":272,"classification":273,"search_phrases":288,"slug":291,"view_count":194,"doi":9,"paper":9,"created_at":292},4768,"江苏省农科院与巴西Embrapa等共建中国—巴西人工智能应用合作中心","https:\u002F\u002Fwww.nongye31.com\u002Fnews\u002F2596.html","江苏省农业科学院与巴西农牧业研究公司Embrapa、南京农业大学、国机数科、国际民众合作协会签署合作谅解备忘录,共建中国—巴西人工智能应用合作中心。中心聚焦中小型智能农机、农业AI平台、作物生长模型等方向,依托巴西家庭农业场景开展技术验证,打造智慧农业南南合作标杆。","2026-10-09 09:09:27来源：江苏省农科院国合处  关键词：智慧农业中巴农业合作农业人工智能阅读量：35\n\n导读：江苏省农科院联合巴西 Embrapa 等多家单位签约共建中国 — 巴西人工智能应用合作中心，聚焦中小型智能农机、农业 AI 平台等联合攻关，依托巴西家庭农业场景开展技术验证与成果转化，打造智慧农业南南合作标杆。\n\n近日，江苏省农业科学院与巴西农牧业研究公司(Embrapa)、南京农业大学、国机数科、国际民众合作协会共同签署合作谅解备忘录，携手共建“中国—巴西人工智能应用合作中心”，以科技创新赋能全球粮食安全与农业现代化。江苏省农科院党委委员、副院长孙洪武出席签约仪式并作主旨发言。\n\n孙洪武重点介绍了江苏省农科院在科技服务与国际合作方面的工作成效。他建议合作各方应落实好人员互访、专家考察、联合研讨等任务，聚焦中小型智能农机、农业AI平台、作物生长模型等方向开展联合攻关，建设好智慧农机服务中心示范点，搭建好人员交流桥梁，携手推进合作中心建设，为深化中巴农业科技合作、保障全球粮食安全、推动农业现代化贡献力量。\n\n巴西农牧业研究公司总裁席尔维娅·马苏鲁哈介绍，Embrapa是拉美地区最大的国家级公立农业科研机构，巴西家庭农户占比超七成，热带农业种植与生产场景丰富，拥有成熟的农业试点研发体系。她期待深度依托合作中心，与包括江苏省农业科学院在内的中方单位深化协作，开展田间技术验证、农业数据共享与技术成果转化，打造金砖农业合作标杆案例。\n\n中国—巴西人工智能应用合作中心将深度整合产学研优势资源，以巴西家庭农业实际应用场景为需求牵引，以中方智慧农业先进技术为重要支撑，推进中巴双方在联合科研攻关、示范基地建设、人才交流培养等方面实现双向赋能、双向落地，探索形成可复制、可推广的智慧农业南南合作新模式。\n\n引用： 共建中国—巴西人工智能应用合作中心 助力智慧农业南南合作 江苏省农业科学院 [引用日期：2026年10月8日]\n\n本文系转载，转发此文目的在于促进信息交流，不存在盈利性目的，此文观点与本站立场无关，不承担任何责任，不做任何投资和交易根据。如无意侵犯媒体或个人知识产权请告知我们，我们将在第一时间做删除处理。\n\n上一篇：混动争锋、沃野竞技！200马力及以上混合动力拖拉机田间实地试验验证活动即将举行","农业商情网 2026-10-08","2026-10-08T00:00:00Z",73,{"impact":106,"substance":18,"depth":17,"authority":205,"freshness":94,"relevant":21,"comment":265},"省级科研机构牵头的中巴智慧农业国际合作，聚焦智能农机与农业AI平台，具行业示范价值，但属签约启动阶段、落地成果尚待观察。",[267],{"name":261,"url":258},[26,28,113,269,270],"南南合作","中巴合作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":274},{"candidates":275,"agricultural_evidence":45},[276,280,284],{"code":32,"score":65,"supported":45,"title":277,"abstract":278,"tags":279},[],[123],[113],{"code":52,"score":65,"supported":45,"title":281,"abstract":282,"tags":283},[],[26],[26],{"code":57,"score":21,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[28],[289,290],"江苏省农科院 Embrapa 人工智能","农业人工智能 中巴合作 南南合作 智慧农业","江苏省农科院Embrapa人工智能-4768","2026-10-13T00:08:35.638471Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":299,"source_url":296,"published_at":300,"category":12,"cover_url":9,"hotness":301,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":311,"primary_topic":316,"secondary_topics":321,"classification":327,"search_phrases":371,"slug":374,"view_count":21,"doi":375,"paper":376,"created_at":386},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":106,"substance":206,"depth":18,"authority":304,"freshness":108,"relevant":21,"comment":305},13,"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。",[307,308],{"name":299,"url":296},{"name":309,"url":310},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[26,28,312,313,314,315],"农业机器人","农业物联网","精准农业","自动驾驶农机",{"code":57,"slug":317,"name":318,"path":319,"parent_code":320},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[322,323],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":236,"slug":324,"name":325,"path":326,"parent_code":320},"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":328},{"candidates":329,"agricultural_evidence":45},[330,337,341,348,355,360,366],{"code":57,"score":18,"supported":45,"title":331,"abstract":333,"tags":336},[332],"artificial intelligence",[334,228,335,332,229],"机器学习","machine 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o'g'li",{"tldr":382,"method":383,"finding":384,"direction":89,"opportunity":385},"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":388,"title":389,"url":390,"summary":391,"summary_zh":9,"content":9,"source_name":392,"source_url":9,"published_at":393,"category":102,"cover_url":9,"hotness":13,"is_selected":45,"score":149,"score_detail":394,"sources":398,"tags":400,"primary_topic":405,"secondary_topics":406,"classification":407,"search_phrases":428,"slug":431,"view_count":194,"doi":9,"paper":9,"created_at":432},3701,"我们的十五五：未来五年，田埂上的新希望","https:\u002F\u002Fbig5.china.com.cn\u002Fgate\u002Fbig5\u002Ff.china.com.cn\u002F2026-09\u002F24\u002Fcontent_118711054.shtml","十五五开局之年国务院印发《加快农业农村现代化十五五规划》。规划首次提出培育壮大农业领域新兴产业和未来产业，推动智能设计育种、农业低空经济等规模化发展，谋划实施农机装备高质量发展、农业生物制造提升、人工智能+农业等行动。要求到2030年农村生活污水治理率达到70%。","中国网","2026-09-24T12:00:00Z",{"impact":395,"substance":396,"depth":107,"authority":153,"freshness":94,"relevant":21,"comment":397},28,23,"国务院层面十五五农业农村现代化规划，首次提出农业未来产业与人工智能+农业行动，政策条款与量化目标明确，权威性和信息增量俱佳，值得进入每日精选。",[399],{"name":392,"url":390},[28,401,402,30,403,404],"十五五规划","农业农村现代化","农业低空经济","智能设计育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":408},{"candidates":409,"agricultural_evidence":45},[410,414,419,423],{"code":32,"score":65,"supported":45,"title":411,"abstract":412,"tags":413},[],[123],[30],{"code":415,"score":65,"supported":45,"title":416,"abstract":417,"tags":418},"J2",[],[402],[402],{"code":57,"score":21,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[28],{"code":424,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},"J1",[],[],[401],[429,430],"人工智能 行动","农业农村现代化 农业人工智能 农业低空经济 智能设计育种","人工智能行动-3701","2026-09-29T00:08:19.404475Z"]