[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-480":3,"related-480":88},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":82,"slug":85,"view_count":86,"doi":8,"paper":8,"created_at":87},480,"农机周讯|聚焦农业人工智能布局、多地整治补贴乱象、行业景气重回扩张区间……","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1060727772_262237","农机周讯8月3日—9日盘点:农业农村部部长张柱在形势政策报告会系统盘点\"十五五\"农机化方向;7月30日至31日农业农村部党组连续召开会议部署加快推进农业人工智能,突破农业传感器专用芯片,健全标准体系与安全管理机制。7月AMI指数52.7%重返景气区间。北京发布\"十五五\"农业农村现代化规划,建成60个智慧农业应用场景;河南完善\"农机云平台\";重庆农科院推出新能源农机移动充换电车及成套核心技术。",null,"河南省农业农村厅公开巡视整改进展，多项举措聚焦农机领域。在补贴方面，调整农机购置与应用补贴范围，引导农户购置紧缺应急装备，并缩短兑付等待时间。在应急保障方面，全省已认定区域农机服务中心832个，组建常态化应急作业服务队。在数字化方面，将完善\"河南农机云平台\"建设，强化顶层设计，提升信息化服务水平。","杰强KITKEUNG News Today 2026-08-10","2026-08-09T16:00:00Z","报道",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,15,12,8,5,1,"省级农机补贴与数字化平台建设进展，信息具体但时效一般。",[25],{"name":10,"url":6},[27,28,29,30,31],"人工智能+农业","农机周讯","AMI","新能源农机","农机云平台",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,63,70,76],{"code":33,"score":19,"supported":52,"title":53,"abstract":55,"tags":56},true,[54],"农业人工智能",[54],[27],{"code":40,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},11,[60],"农机",[60],[],{"code":64,"score":65,"supported":52,"title":66,"abstract":67,"tags":69},"I1",3,[],[68],"智慧农业",[],{"code":71,"score":65,"supported":52,"title":72,"abstract":73,"tags":75},"I5",[],[74],"农业传感",[],{"code":77,"score":65,"supported":52,"title":78,"abstract":79,"tags":81},"J2",[],[80],"农业农村现代化",[],[83,84],"人工智能+农业 农机云平台 新能源农机 农机周讯","人工智能+农业 农机云平台","人工智能+农业农机云平台新能源农机农机周讯-480",4,"2026-08-13T23:53:44.084998Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,192,253,328,374,430],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":8,"source_name":97,"source_url":94,"published_at":98,"category":99,"cover_url":8,"hotness":100,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":112,"primary_topic":117,"secondary_topics":118,"classification":124,"search_phrases":173,"slug":176,"view_count":22,"doi":177,"paper":178,"created_at":191},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","论文",25,81,{"impact":103,"substance":104,"depth":17,"authority":105,"freshness":20,"relevant":22,"comment":106},22,20,13,"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。",[108,109],{"name":97,"url":94},{"name":110,"url":111},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[68,54,113,114,115,116],"农业机器人","农业物联网","精准农业","自动驾驶农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[119,120],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":71,"slug":121,"name":122,"path":123,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":46,"locked":14,"version":47,"method":48,"evidence":125},{"candidates":126,"agricultural_evidence":52},[127,136,144,151,158,162,168],{"code":33,"score":17,"supported":52,"title":128,"abstract":130,"tags":135},[129],"artificial intelligence",[131,132,133,129,134],"机器学习","计算机视觉","machine learning","computer vision",[54],{"code":40,"score":137,"supported":52,"title":138,"abstract":140,"tags":143},17,[139],"agricultural machinery",[141,142,139],"农业机械","收割机",[],{"code":71,"score":13,"supported":52,"title":145,"abstract":146,"tags":150},[],[147,148,149],"物联网","internet of things","IoT",[114],{"code":152,"score":89,"supported":52,"title":153,"abstract":154,"tags":157},"I3",[],[155,156],"遥感","remote sensing",[],{"code":64,"score":21,"supported":52,"title":159,"abstract":160,"tags":161},[],[115],[68,115],{"code":163,"score":65,"supported":52,"title":164,"abstract":165,"tags":167},"F4",[],[166],"除草",[],{"code":169,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},"F6",[],[],[113],[174,175],"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":177,"openalex_id":179,"authors":180,"venue":97,"cited_by_count":183,"oa_url":94,"card":184,"direction":188,"ingested_from":190},"W7221138004",[181],{"name":182,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",0,{"tldr":185,"method":186,"finding":187,"direction":188,"opportunity":189},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-10T23:30:13.826410Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":8,"content":8,"source_name":197,"source_url":195,"published_at":198,"category":99,"cover_url":8,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":211,"search_phrases":233,"slug":236,"view_count":22,"doi":237,"paper":238,"created_at":252},2909,"Field-scale prediction and spatial mapping of tractor tractive efficiency in tillage operations using machine learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10443-5","Field-scale prediction and spatial mapping of tractor tractive efficiency in tillage operations using machine learning。Precision Agriculture","Precision Agriculture","2026-09-18T00:00:00Z",71,{"impact":19,"substance":104,"depth":137,"authority":201,"freshness":20,"relevant":22,"comment":202},14,"发表于核心期刊 Precision Agriculture 的研究，用机器学习实现田块尺度拖拉机牵引效率预测与空间制图，方法新颖、对精准耕作有实用价值，但属细分领域进展，公共影响有限。",[204],{"name":197,"url":195},[68,54,115,206,207],"土壤耕作","农机作业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[210],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":212},{"candidates":213,"agricultural_evidence":52},[214,218,223,228],{"code":33,"score":19,"supported":52,"title":215,"abstract":216,"tags":217},[133],[133],[54],{"code":40,"score":58,"supported":52,"title":219,"abstract":221,"tags":222},[220],"tractor",[220],[],{"code":64,"score":21,"supported":52,"title":224,"abstract":225,"tags":227},[],[226],"precision agriculture",[68,115],{"code":229,"score":22,"supported":14,"title":230,"abstract":231,"tags":232},"H2",[],[],[206],[234,235],"拖拉机 牵引效率 机器学习","耕作 牵引效率 空间制图","拖拉机牵引效率机器学习-2909","10.1007\u002Fs11119-026-10443-5",{"doi":237,"openalex_id":239,"authors":240,"venue":197,"cited_by_count":183,"oa_url":8,"card":8,"direction":251,"ingested_from":190},"W7213532695",[241,243,245,247,249],{"name":242,"orcid":8},"So-Yun Gong",{"name":244,"orcid":8},"Seung-Min Baek",{"name":246,"orcid":8},"Seung-Yun Baek",{"name":248,"orcid":8},"Yong-Joo Kim",{"name":250,"orcid":8},"Wan-Soo Kim","农业人工智能与决策模型","2026-09-19T23:30:03.382845Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":8,"content":8,"source_name":258,"source_url":8,"published_at":259,"category":99,"cover_url":8,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":270,"secondary_topics":276,"classification":279,"search_phrases":314,"slug":317,"view_count":318,"doi":8,"paper":319,"created_at":327},2494,"露地白萝卜定植收获农机农艺垂直大模型调优方法研究(NRA-LoRA, Qwen3-4B LLM-Metric 84.32)","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166270.html","山东农业大学机械与电子工程学院李扬、褚化星、李天华、赵维松、潘浩晨、王善平,山东省设施园艺智慧生产技术装备重点实验室,农业农村部南京农业机械化研究所,山东省果树研究所联合发表。针对通用大语言模型在露地白萝卜定植与收获场景中专业性不足、农机农艺协同能力弱等问题,提出融合农机农艺知识垂直大模型调优方法。构建包含农机、农艺、农机农艺关联和定植收获关联 4 类知识专用数据集,共 14524 条数据;提出非均匀 Rank 分配的 NRA-LoRA 微调方法。Qwen3-4B(NRA-LoRA)表现最优,LLM-Metric 达 84.32,BLEU-4\u002FROUGE-1\u002F2\u002FL 分别 30.95\u002F52.68\u002F23.84\u002F41.96。","农机化研究 2026(11) 2026-09-10","2026-09-09T16:00:00Z",75,{"impact":18,"substance":103,"depth":17,"authority":201,"freshness":89,"relevant":22,"comment":262},"面向露地白萝卜定植收获的农机农艺垂直大模型微调研究，方法新颖、数据规模可观，属细分领域实质性技术进展，但应用面较窄，适合作为主题聚合素材而非头条精选。",[264],{"name":258,"url":256},[68,54,266,267,268,269],"智能农机","大语言模型","农机农艺融合","白萝卜",{"code":271,"slug":272,"name":273,"path":274,"parent_code":275},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[277,278],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":280},{"candidates":281,"agricultural_evidence":52},[282,288,293,298,304,310],{"code":40,"score":283,"supported":52,"title":284,"abstract":285,"tags":287},19,[60],[60,141,286],"机械化",[268,266],{"code":33,"score":105,"supported":52,"title":289,"abstract":291,"tags":292},[290],"大模型",[290],[54,267],{"code":271,"score":58,"supported":52,"title":294,"abstract":296,"tags":297},[295],"萝卜",[295],[],{"code":299,"score":65,"supported":52,"title":300,"abstract":301,"tags":303},"B2",[],[302],"果树",[],{"code":305,"score":65,"supported":52,"title":306,"abstract":307,"tags":309},"B7",[],[308],"设施园艺",[],{"code":64,"score":22,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[68],[315,316],"农业人工智能 农机农艺融合 大语言模型 智慧农业","农业人工智能 农机农艺融合","农业人工智能农机农艺融合大语言模型智慧农业-2494",2,{"doi":8,"openalex_id":8,"authors":320,"venue":8,"cited_by_count":183,"oa_url":8,"card":321,"direction":251,"ingested_from":326},[],{"tldr":322,"method":323,"finding":324,"direction":251,"opportunity":325},"提出NRA-LoRA微调方法，构建农机农艺知识数据集，提升白萝卜定植收获垂直大模型性能。","构建14524条农机农艺知识数据集，采用非均匀Rank分配的NRA-LoRA微调","Qwen3-4B(NRA-LoRA)最优，LLM-Metric达84.32，BLEU-4\u002FROUGE","可探索非均匀Rank分配在更多作物农机农艺场景的泛化，及知识图谱增强的垂直大模型。","agent","2026-09-15T00:04:27.326828Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":8,"content":333,"source_name":334,"source_url":8,"published_at":335,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":341,"tags":343,"primary_topic":348,"secondary_topics":349,"classification":351,"search_phrases":369,"slug":372,"view_count":65,"doi":8,"paper":8,"created_at":373},773,"[黑龙江省农科院]黑龙江省农业机械学会2026年学术年会暨「推动国产高端智能农机装备高质量发展」研讨会召开——刘劼分享后大模型时代智慧农场技术","https:\u002F\u002Fwww.haas.cn\u002Finfo\u002F6401\u002F335281.htm","黑龙江省农业机械学会2026年学术年会8月17日在哈尔滨召开,中国农业机械化科学研究院总工程师方宪法、哈尔滨工业大学人工智能研究院院长刘劼、山东省农机院党委书记郭洪恩等三位专家主旨报告覆盖高端智能农机发展展望、精准作业技术、无人机应用、智能检测装备、智能管理、自动驾驶平台等多个前沿领域。8月18日上午与会代表前往省农科院民主园区示范展示基地参观以「科技赋能农机·创新驱动未来」为主题的展览,集中展示重型铰接式鲜食玉米收获机、电驱气力式精密播种机、白浆层培肥机等装备。","8月17日，黑龙江省农业机械学会2026年学术年会暨“推动国产高端智能农机装备高质量发展”研讨会在哈尔滨召开。本次会议以“科创驱动·智启新程——推动国产高端智能农机装备高质量发展”为主题，来自全国各地的农机领域专家学者、企业家及会员代表齐聚冰城，围绕高端智能农机装备技术创新与产业升级展开多层次、全方位研讨交流。\n\n主旨报告环节，中国农业机械化科学研究院总工程师、首席专家方宪法作了题为《农业装备技术进展》的报告，系统梳理了国内外农业装备技术发展历程与前沿趋势。哈尔滨工业大学人工智能研究院院长刘劼以《后大模型时代的智慧农场技术》为题，分享了人工智能在智慧农业领域的最新研究成果与应用实践。山东省农业机械科学研究院党委书记、理事长郭洪恩分享了科技助力山东智能农机装备高质量发展的典型经验。三位专家的报告视野宏阔、内容前沿，为与会代表带来了深刻的学术启迪。\n\n黑龙江省农机产业技术创新推广体系首席专家郭雪峰、黑龙江八一农垦大学工程学院院长马永财、沈阳农业大学工程学院副院长邬立岩、黑龙江省农机院智能化研究所所长吴泽全、东北农业大学教授、博导王一甲、黑龙江德沃科技开发有限公司副总经理邓宇等在研讨会上分别发言。\n\n全场报告涵盖高端智能农机发展展望、精准作业技术、无人机应用、智能检测装备、智能管理、自动驾驶平台等多个前沿领域，展现了我国智能农机装备领域的最新研究成果与产业动态。\n\n8月18日上午，与会代表前往省农科院民主园区示范展示基地，参观以“科技赋能农机·创新驱动未来”为主题的展览。展览现场集中展示了近年来黑龙江省农业机械工程科学研究院研发推广的各类高端智能农机装备，涵盖寒地旱田及水田作业装备、小区育种装备、黑土保护装备、绿色种养循环装备、特色作物装备、农机智能化仪器及航空植保与农情监测设备。代表们近距离观摩了重型铰接式鲜食玉米收获机、电驱气力式精密播种机、白浆层培肥机、育种试验小区精量播种机、固态\u002F液态有机肥撒施机、青贮打捆包膜一体机、农机试验检测系列仪器设备等前沿装备，切身感受了黑龙江省农机装备智能化、绿色化、高端化发展的丰硕成果。\n\n与会代表纷纷表示，本次学术年会规格高、内容实、成果丰，既是一次学术思想的碰撞，也是一次产业实践的检阅。当前，随着中央一号文件各项部署逐步落实，“优机优补”政策全面落地，高端农机市场加速洗牌。本次会议的召开，进一步凝聚了行业共识，明确了发展方向，必将为推动国产高端智能农机装备高质量发展注入新的动力。\n\n本次会议由黑龙江省农业机械学会主办，黑龙江省科学技术协会支持，中国农业机械学会农机维修分会、农业农村部旱地农业装备技术重点实验室协办，黑龙江省农业机械工程科学研究院承办。","黑龙江省农业科学院 2026-08-18","2026-08-18T00:00:00Z",69,{"impact":104,"substance":338,"depth":201,"authority":19,"freshness":339,"relevant":22,"comment":340},16,7,"省级农机学术年会，聚焦高端智能农机装备，内容充实，但影响范围以省内为主。",[342],{"name":334,"url":331},[266,344,345,346,347],"黑龙江省农科院","后大模型时代","哈尔滨工业大学","高端农机",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[350],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":352},{"candidates":353,"agricultural_evidence":52},[354,359,363],{"code":40,"score":355,"supported":52,"title":356,"abstract":357,"tags":358},26,[60,141],[60,141,286],[266],{"code":33,"score":20,"supported":52,"title":360,"abstract":361,"tags":362},[290],[],[],{"code":364,"score":65,"supported":52,"title":365,"abstract":366,"tags":368},"A3",[],[367],"玉米",[],[370,371],"哈尔滨工业大学 黑龙江省农科院 后大模型时代 智能农机","哈尔滨工业大学 黑龙江省农科院","哈尔滨工业大学黑龙江省农科院后大模型时代智能农机-773","2026-08-21T00:05:56.133112Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":8,"content":8,"source_name":379,"source_url":8,"published_at":380,"category":99,"cover_url":8,"hotness":13,"is_selected":14,"score":381,"score_detail":382,"sources":384,"tags":386,"primary_topic":393,"secondary_topics":399,"classification":402,"search_phrases":425,"slug":428,"view_count":89,"doi":8,"paper":8,"created_at":429},248,"改进YOLO与无人机组群监测水稻病虫害施药规划——浙江省诸暨市气象局《南方农机》2026年08期","https:\u002F\u002Fwap.cnki.net\u002Ftouch\u002Fweb\u002FJournal\u002FArticle\u002FNFLJ202608016.html","针对水稻田间病虫害监测效率低、施药粗放等问题,文章提出一种改进YOLO模型与无人机组群协同的一体化解决方案。研究通过引入CBAM注意力机制、优化特征融合结构及改进损失函数,提升复杂背景下小目标病虫害检测精度;构建基于栅格划分的无人机组群监测系统,结合5G通信实现田间实时数据传输与智能决策。研究将传统粗放施药模式升级为基于监测数据的精准靶向施药,有效减少农药使用量与人工成本,显著提升防治效率和作业精度,为水稻病虫害智能监测与精准施药提供一体化技术方案,推动植保作业向智能化、精准化方向转型升级。","南方农机 2026年08期","2026-08-01T00:00:00Z",55,{"impact":19,"substance":18,"depth":201,"authority":13,"freshness":86,"relevant":22,"comment":383},"地方气象局论文，提出改进YOLO与无人机组群监测水稻病虫害方案，有一定技术细节，但影响范围有限，时效性一般。",[385],{"name":379,"url":377},[387,388,389,390,391,392],"无人机","改进YOLO","水稻病虫害","精准施药","CBAM","南方农机",{"code":394,"slug":395,"name":396,"path":397,"parent_code":398},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[400,401],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":403},{"candidates":404,"agricultural_evidence":52},[405,410,415,419],{"code":394,"score":18,"supported":52,"title":406,"abstract":407,"tags":409},[396],[396,408],"稻田",[389],{"code":33,"score":58,"supported":52,"title":411,"abstract":413,"tags":414},[412],"YOLO",[412],[],{"code":40,"score":20,"supported":52,"title":416,"abstract":417,"tags":418},[60],[],[],{"code":163,"score":339,"supported":52,"title":420,"abstract":421,"tags":424},[],[422,423],"农药","植保",[390],[426,427],"水稻病虫害 南方农机 精准施药 无人机","水稻病虫害 南方农机","水稻病虫害南方农机精准施药无人机-248","2026-08-05T23:53:02.480482Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":8,"content":435,"source_name":436,"source_url":8,"published_at":437,"category":438,"cover_url":8,"hotness":100,"is_selected":52,"score":439,"score_detail":440,"sources":443,"tags":448,"primary_topic":452,"secondary_topics":453,"classification":454,"search_phrases":474,"slug":477,"view_count":22,"doi":8,"paper":8,"created_at":478},4380,"今年「三秋」生产有更多高端智能农机投入应用","https:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202610\u002Ft20261007_3248745.shtml","农业农村部 10-7 介绍,「十五五」以来我国推动大型高端智能农机稳步升级,18 公斤\u002F秒喂入量谷物联合收获机、85 公斤载荷植保无人机加快产业化;6-15 度丘陵山地拖拉机已在甘肃、重庆、四川加快推广,山地玉米播种机投入西南、西北生产一线;200-300 马力混动拖拉机、25-75 马力纯电动拖拉机已投入生产,大幅提升「三秋」作业效率。","## 今年“三秋”生产有更多高端智能农机投入应用\n\n2026年10月07日 16:08 来源：新华社\n\n新华社北京10月7日电（记者胡璐）当前正值秋收秋种关键时节。记者7日从农业农村部了解到，今年“三秋”生产有更多高端智能农机产品投入一线应用。\n\n据了解，“十四五”时期，我国农业机械化发展成效显著。进入“十五五”以来，我国推动大型高端智能农机稳步升级，大幅提升“三秋”作业效率。18公斤\u002F秒喂入量谷物联合收获机、85公斤载荷植保无人机等加快产业化应用。\n\n在丘陵山区，适用农机也在加快应用，坡耕地、梯台田实现“有好机用”。目前6至15度丘陵山地拖拉机正在甘肃、重庆、四川等地加快推广应用，山地玉米播种机已在西南、西北地区投入生产一线。\n\n此外，新能源农机加速发展，有力推动农业生产绿色发展、降本增效。当前200至300马力段混动拖拉机、25至75马力段纯电动拖拉机产品已投入生产，混动水稻收获机、混动6行打包采棉机近期将定型量产。\n\n(责任编辑：符仲明)","新华社 中国经济网 2026-10-07","2026-10-07T00:00:00Z","政策",85,{"impact":355,"substance":104,"depth":18,"authority":18,"freshness":441,"relevant":22,"comment":442},9,"央媒报道全国三秋生产中高端智能农机、丘陵山区适用机具与新能源农机的产业化进展，含具体机型与马力段数据，政策与产业参考价值高。",[444,445],{"name":436,"url":433},{"name":446,"url":447},"新华社 2026-10-07","https:\u002F\u002Fwww.agricoop.cn\u002Fnews\u002Fshow.php?itemid=33134",[266,449,30,450,451],"农业机械化","三秋生产","丘陵山区农机",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":455},{"candidates":456,"agricultural_evidence":52},[457,461,465,469],{"code":40,"score":201,"supported":52,"title":458,"abstract":459,"tags":460},[60],[60],[451,449,266],{"code":364,"score":65,"supported":52,"title":462,"abstract":463,"tags":464},[],[367],[],{"code":163,"score":65,"supported":52,"title":466,"abstract":467,"tags":468},[],[423],[],{"code":470,"score":22,"supported":14,"title":471,"abstract":472,"tags":473},"J4",[],[],[450],[475,476],"农业农村部 高端智能农机 三秋","丘陵山地拖拉机 甘肃 重庆","农业农村部高端智能农机三秋-4380","2026-10-08T00:06:06.782604Z"]