[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2446":3,"related-2446":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":39,"search_phrases":82,"slug":85,"view_count":86,"doi":8,"paper":8,"created_at":87},2446,"智能农机装备研发创新与验证推广实务培训班 9-20~9-23 杭州萧山举办","http:\u002F\u002Fwww.amic.agri.cn\u002FsecondLevelPage\u002Finfo\u002F3\u002F224557","农业农村部农业机械化总站发出通知(农机化总站〔2026〕81 号),拟于 2026 年 9 月 20 日至 9 月 23 日在浙江省杭州市萧山区举办'智能农机装备研发创新与验证推广实务培训班'。培训内容涵盖农业人工智能发展专题(农业机器人、智能农机标准体系等)、智能农机关键核心零部件与算法产业化应用(农业芯片、机器视觉、北斗定位导航等)、典型应用场景(大田、畜禽、水产、设施种植)以及实地观摩与农业机器人现场展示。培训对象包括各省农机中心、试验鉴定机构、推广机构、生产企业等。",null,"农机化总站〔2026〕81号\n\n各相关单位：\n\n为贯彻落实中央一号文件精神及农业农村部关于加快农业人工智能发展的系列部署，深入推进人工智能与农机融合发展，着力提升智能农机装备的自主创新水平，发挥典型应用场景的示范引领作用，总站拟于2026年9月20日至9月23日在浙江省杭州市萧山区举办“智能农机装备研发创新与验证推广实务培训班”。现将有关事项通知如下。\n\n一、培训内容\n\n（一）农业人工智能发展专题报告\n\n1、“人工智能+农机”发展现状与趋势\n\n2、农业机器人发展实践与应用\n\n3、智能农机装备标准体系架构\n\n4、智能机器人安全实践对农机装备智能化发展的启示\n\n5、智能网联汽车关键核心技术对智能农机装备的借鉴与启示\n\n（二）智能农机关键核心零部件与算法产业化应用专题报告\n\n1、农业芯片核心技术发展与产业应用现状分析\n\n2、农业传感器在智能农机装备和农业机器人中的实践与应用\n\n3、机器视觉设备与算法在智能农机发展中的应用\n\n4、北斗定位与导航技术在农业精准作业领域中的应用\n\n5、智能农机串行控制和通信数据网络技术要求\n\n（三）智能农机装备在典型应用场景中的实践专题报告\n\n1、大田智能农机通用性关键性技术与装备\n\n2、畜禽养殖智能化技术与装备\n\n3、水产智慧养殖机械化技术与装备\n\n4、设施种植智能化机械装备集成与应用\n\n（四）人工智能与大数据应用场景实地观摩与农业机器人现场展示交流\n\n二、培训对象\n\n各省（自治区、直辖市）农机中心、农机试验鉴定机构、农机化技术推广机构、高等院校、科研院所、行业协会、检测机构、智能农机装备生产制造企业、农业生产经营主体、金融投资机构等相关人员。\n\n三、时间地点\n\n（一）培训时间：9月20日报到，9月21日至9月22日培训，9月23日疏散。\n\n（二）培训地点：杭州市萧山区宝盛宾馆（浙江省杭州市萧山区市心中路618号，联系电话：潘瑶 15906715852，洪燕芳 13588705700）。\n\n四、报名及培训费用\n\n（一）报名方式。请使用微信扫描下方二维码，按要求填写信息后提交。报名截止时间为9月16日。\n\n![Image 1](http:\u002F\u002F202.127.42.160:3009\u002Fupload_file\u002F2026\u002F08\u002F10\u002F20260810101025836.png)\n\n报名二维码\n\n（二）培训费用。培训班收取培训费2000元\u002F人(含培训、教材、培训证书、用餐等费用)，餐饮统一安排，交通费、住宿费（340元\u002F晚）自理，不安排接送站。\n\n（三）缴费方式。培训费采用银行汇款或报到时刷银行卡、支付宝或微信支付，现场不收取现金。银行汇款请务必在备注栏标明“智能农机培训”，并在报到时出示汇款凭证复印件，汇款单位名称与开票单位名称需保持一致，如不一致，还请备注参训人员单位名称、姓名，并开具证明提供发票信息。\n\n收款单位：农业农村部农业机械化总站；\n\n开户银行：中国农业银行股份有限公司北京十里河支行；\n\n银行账号：11220701040017219。\n\n五、其他事项\n\n（一）为统筹农业机器人室外展区布展工作，请有意参展的单位于2026年8月31日（含）前将产品资料（产品图片、实地作业视频、关键技术介绍等）、企业联系人及联系方式发送至邮 箱njznc@agri.gov.cn。我们将对所有报送材料进行择优筛选，并于9月7日（含）前向通过遴选的对象发出布展通知。\n\n（二）请参训学员严格遵守培训纪律，培训期间注意人身和财产安全。\n\n（三）总站将为参训学员颁发培训证书。\n\n（四）培训期间，要严格落实中央八项规定及其实施细则精神，不得安排与培训内容无关的事项，严禁外出参加各种聚会、拜访、宴请等活动，不得饮酒。\n\n（五）联系人：\n\n李 苗 010-59199179，18753469583；\n\n赵泽明 010-59199016，15201628716。\n\n附件：乘车路线\n\n农业农村部农业机械化总站\n\n2026年8月6日\n\n附件\n\n乘车路线\n\n一、杭州萧山国际机场。酒店距萧山国际机场约22公里。\n\n1.打车\u002F网约车，30—40分钟，费用：预估30—45元；\n\n2.地铁，“萧山国际机场”乘地铁7号线“吴山广场”方向至“建设三路”，同站转2号线“朝阳”方向至“人民广场”D口出站，往南步行约10分钟。\n\n二、杭州东站。酒店距杭州东站约16公里。\n\n1.打车\u002F网约车，30—40分钟，费用：预估25—35元；\n\n2.地铁，“火车东站”乘地铁4号线“浦沿”方向至“钱江路”，同站转2号线“朝阳”方向至“人民广场”D口出站，往南步行约10分钟。\n\n三、杭州站。酒店距杭州站约15公里。\n\n1.打车\u002F网约车，30—40分钟，费用：预估35—50元；\n\n2.地铁，“城站”乘地铁5号线“姑娘桥”方向至“人民广场”D口出站，往南步行约10分钟。\n\n四、杭州南站。酒店距杭州南站约5公里。\n\n1.打车\u002F网约车，15—25分钟，费用：预估20—30元；\n\n2.地铁，“火车南站”乘地铁5号线“南湖东”方向至“人民广场”D口出站，往南步行约10分钟。\n\n五、杭州西站。酒店距杭州南站约40公里。\n\n1.打车\u002F网约车，80—90分钟，费用：预估100—160元；\n\n2.地铁，“火车西站”乘地铁19号线“永盛路”方向至“沈塘桥”，同站转2号线“朝阳”方向至“人民广场”D口出站，往南步行约10分钟。","中国农业机械化信息网 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智能农机加速下乡","http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F202610\u002Ft20261016_3260414.shtml","《山西省智慧农业行动计划（2026—2030年）》细化落地举措，围绕丘陵山地特点推进智慧农业先行先试、智慧农（牧、渔）场建设与技术创新。全省坡耕地1686.66万亩、占耕地总面积29.06%，为此加强丘陵山区智能农机研发引进，开展丘陵山区杂粮农机具研究，组建省级智慧农业产业技术体系。","**阅读提示**\n\n**我省依托科研院所与高校、企业联合攻关，量身研发本土化、适配型智能农机装备。当前，智能技术稳步融入耕种管收储全产业链条，推动农业生产实现精准管控、降本增效。同时，相关部门统筹推行农业社会化服务、落实专项政策补贴、开展定向技能培训等多项举措，打通技术落地堵点，构建智慧农业发展体系，让数字红利惠及广袤田野，书写黄土高原现代农业转型发展新答卷。**\n\n晨光破晓，阳光渐暖。太原市小店区北格镇梁家庄村的智慧农业大棚内，智能设备精准感知田间环境变化，保温卷帘随气温变化平稳升降，全程无需人工操作。\n\n这座由山西优班图科技股份有限公司联合山西农业大学（省农科院）共建的实验基地，是我省设施农业智能化升级的生动缩影，见证着传统农业向智慧农业转变的迭代蜕变。\n\n“大棚卷帘并非一次性全开，而是根据温度变化幅度循序渐进调节。”基地负责人李鹏飞介绍。大棚内各类传感器全天候实时采集温湿度、光照强度、二氧化碳浓度等核心环境数据，通过智能系统自动联动卷帘、通风、灌溉等设备，实现种植环境动态精准调控，彻底颠覆了传统“看天种地”的粗放模式，全面迈入“数据管棚”的智慧农业新阶段。\n\n农业新质生产力，核心是数字技术与农业生产的深度融合。2026年中央一号文件明确提出，要因地制宜发展农业新质生产力，促进人工智能与农业发展相结合，拓展无人机、物联网、机器人等应用场景。对标国家部署，《山西省智慧农业行动计划（2026—2030年）》细化落地举措，加快数字技术与农业深度融合，提升农业智能化水平，提高农业生产效率、资源利用率和农民收入，推动山西现代农业高质量发展。\n\n依托政策牵引、科研攻关、产业赋能，三晋大地各类智能农机、数字装备加速落地，贯穿农业生产全链条，为特色种养提质增效、群众稳定增收注入数字动能。\n\n**立足实际**\n\n**研发适配型智能农机**\n\n我省丘陵山地广、零散坡耕地多。第三次国土调查数据显示，全省坡耕地达1686.66万亩，占耕地总面积29.06%；梯田耕地达1421.68万亩，占耕地总面积24.49%。地块零散、坡度不均、田间道路狭窄，适配小块坡耕地的专用农机短缺，是丘陵山区农业提质增效的主要瓶颈。\n\n靶向破解本土生产难题，我省聚焦农业生产薄弱环节，联动科研机构、涉农企业、专业合作社精准攻关，量身研发适配丘陵山区作业的智能农机装备，补齐山区农业机械化、智能化短板。\n\n8月20日，娄烦县天池店乡南岔村马铃薯新优品标准化种植示范基地内，一垄垄马铃薯长势喜人，智能化种植设备的赋能成效尽显。\n\n“马铃薯长势旺盛，丘陵山区自走式马铃薯种植机发挥了重要作用。”娄烦润和美种植专业合作社负责人尤同义说。这款机具由山西省薯类产业技术体系专家赵旭志、贺孝兵团队研发，可一次性完成开沟、播种、起垄、镇压等一体化作业，通过起垄镇压保墒技术有效提升出苗率，精准解决了丘陵山区小地块大中型农机难进地、小型农机功能单一的行业痛点。\n\n不只马铃薯种植装备，多款适配山区地形的智能农机相继落地推广。针对农业生产各类细分痛点，山西农机科创持续发力、多点突破。\n\n由山西农业大学（省农科院）高粱研究所研究员白文斌团队研发的“大地智牛”全自主覆膜播种机器人，已在阳高、寿阳、襄垣、武乡、永和等多个山区县2000余亩耕地验证使用。该设备集成施肥、覆膜、铺设滴灌带、播种、覆土五道工序，可在6°及以下坡度耕地稳定作业，填补了国内丘陵山区覆膜播种智能农机的空白。设备作业时速稳定，可根据不规则曲线地块自主规划作业路径、动态调整速度，兼顾作业精度与生产效率。\n\n山西沃勃农业发展有限公司与山西农业大学（省农科院）园艺学院等科研部门，聚焦温室果蔬采摘用工紧缺、成本偏高、果实损伤率高的难题，融合多模态视觉与三维定位技术，成功研发履带式自主导航采摘机器人，实现果实精准识别定位、机械臂柔性低损采摘。一台机器人可替代2—3名采摘工，单棚年节约人力成本1.6万元以上。\n\n围绕玉米全生育期绿色除草需求，五台县城园丰农机制造有限公司联合清华大学天津高端装备研究所等单位，集成全地形移动平台、多模态感知组件、智能决策控制器、高精度导航系统、仿生物理除草与松土机构、复合能源系统及物联网作业平台，成功制造覆膜玉米智能除草机器人。该款机器人可自主建立地块与作物行地图，精准识别玉米苗株、杂草、地膜和障碍物，在实现清除田间杂草的同时完成土壤浅松。\n\n太原科技大学和山西农谷现代农业装备科技有限公司等单位，成功研制黄花菜智能采摘机器人。此款采摘机器人依托国际领先的并联机器人技术，可保障高速高精度作业；视觉系统利用高感光相机与多光谱融合增强方法，能快速识别花蕾进行轨迹规划；末端执行器采用了力位混合控制技术，可精准定位采摘点实现无损采摘；控制系统融合激光雷达等多传感器，可实现复杂田块自主导航与动态避障。\n\n一系列本土化、适配型智能农机的研发与落地，精准破解了我省丘陵山地农业生产的诸多短板，为推动山地农业从传统人工劳作向智能高效作业转型提供了全新选择。\n\n**全链赋能**\n\n**贯穿耕管收储全环节**\n\n8月23日，运城市盐湖区龙居镇南花村百忍堂冷库分选车间内，智能分拣设备全速运转，一颗颗红香酥梨经由传送轨道有序输送，通过多维度智能扫描，系统综合比对果体大小、重量、色泽、表皮瑕疵、内部品质等多项参数，依托智能算法自动分级、精准归类，实现标准化分选。\n\n“人工分拣没有统一标准，果品品相、大小参差不齐，很多优质梨果因分拣不规范，卖不上高端市场价。”冷库负责人张蓓坦言。智能分拣设备的投入使用，彻底补齐人工分拣的短板，让优质果品匹配高端价值，有效助力农户增收、产业提质。如今，智能化、数字化技术已全面渗透农业种植、田间管护、采收加工、仓储保鲜全链条，从单一设备示范应用，转向全流程、系统化智慧升级。\n\n田间管护更精细。春季小麦管护关键期，曲沃县曲村镇下坞村麦田里，搭载北斗定位的自由式喷灌机器人灵活作业，180米直径喷射范围实现360°无死角精准供水。相较于传统灌溉模式，设备节水55%左右，作业效率提升85%以上，既省水省力，又保障麦苗均匀受水、长势均衡。小麦灌浆期，临猗县统筹8支专业飞防队伍、119架植保无人机，对全县26.97万亩小麦开展“一喷三防”全覆盖作业，无人机按精准航线低空作业，均匀喷施药剂与叶面肥，筑牢粮食丰产防线。\n\n育苗耕种更高效。山西六新科技有限公司等单位联合研发的数智育苗机器人，搭载双模式自由切换、地块仿形自适应两大核心技术。设备可快速切换条播、撒播作业模式，自动根据苗圃凹凸地表调整播种高度，保证落种均匀；内置定量分料结构，适配大小不同农作物、蔬菜、林木、药材种子，可同步完成播种、覆土、轻度镇压一体化作业，整机机动灵活，适合露天苗圃大范围连续作业。\n\n田间作业更智能。山西恒赫百旺智能科技有限公司研发的丘陵山地无人设备，集成智能感知、精准决策、自主执行功能，可智能生成与坡地、梯田契合的自主飞行航线，实现定高、定速全自主飞行与定量施药作业。\n\n仓储保鲜更安全。太原先元科技有限公司等联合打造的粮食干燥仓储全流程管理系统，以中央集控调度机器人为核心，可智能识别粮食水分，动态调节作业温度、风量，精准完成粮食干燥处理；仓储阶段可24小时自动监测粮情、智能翻粮，全方位保障粮食存储安全。\n\n为持续拓宽智慧农业应用场景，今年7月，省工信厅、省农业农村厅联合发文，聚焦种植、养殖、农产品初加工三大重点领域，启动农业领域机器人典型应用场景遴选工作，以标杆场景引领农机装备创新迭代，推动全省农业机械化、智能化高质量发展。\n\n在北斗导航、智能感知、精准算法等技术支持下，智慧农机已成为三晋大地耕、种、管、收的生力军。\n\n左权县探索建立“县级统筹、农机到户、技术护航、补贴直达”四位一体工作模式，投放300余台配备北斗导航、无人驾驶功能的智能农机，实现耕、种、管、收全链条作业。据该县农业农村局工作人员介绍，智能农机依托导航系统规划作业路径，行距误差控制在2厘米以内，作业深度偏差不超1厘米，可有效杜绝漏耕、重耕问题，种子利用率提升15%以上，农业生产精细化、标准化水平显著提升。\n\n怀仁市依托智慧农机载体，以数据互联互通为支撑，搭建起涵盖耕、种、管、收各环节的智慧农事服务体系，助力当地农业由传统“会种地”向现代“慧种地”迭代升级，大力推行可感知、可控制、可决策的智慧农业发展模式。该市农业机械化发展中心主任郭亮介绍，自2023年起，当地逐步推广农机无人驾驶辅助系统，现有设备保有量约200套，有效降低人工与农资投入成本。\n\n**破解堵点**\n\n**构建长效发展体系**\n\n智能农机、数字装备的广泛应用，为我省农业转型升级、提质增效提供了有力支撑，但高端智能设备造价偏高、小农户购置能力不足等问题，仍然制约着全省智慧农业的规模化普及、全覆盖推广。\n\n“公司研发的履带式智能植保无人机可自主规划路线、24小时不间断作业，性能稳定高效，但价格偏高，目前主要推广于广西、海南等规模化果园种植区，省内仅万荣、临猗等林果大县落地应用。”山西添翼创新科技有限公司总经理闫太平道出行业共性难题。\n\n山西优班图科技股份有限公司等联合研发的智能水肥药一体机同样面临推广困境，设备可根据作物长势精准施肥喷药、节本增效，却因购置成本较高，让众多小农户望而却步。\n\n针对单家独户购置成本高、设备利用率低的痛点，各地立足小农户多、地块碎片化的农业实情，创新探索“智能农机共享+农业生产托管”模式，将高端智能装备转化为社会化服务的“公共农具”，让小农户便捷享受数字农业红利。\n\n社会化服务提质增效，助力普惠共享。临汾市翼城县益农区域农机社会化服务中心，依托全套智能农机装备开展规模化农业托管服务，全程托管土地2万余亩，单环节托管超10万亩，辐射带动周边大量小农户。通过统一智能化作业，实现亩均节本50元以上、增产15%，相关实践成功入选全国农机社会化服务典型案例，为全省智慧农机普惠化推广提供可借鉴的经验。\n\n政策精准托举，打通落地壁垒。相关部门积极落实农机购置与应用补贴政策，将农用无人机、田间智能监测设备、智能耕作机械等新型装备纳入补贴范围，推行优机优补、精准补贴，大幅降低经营主体购置成本。\n\n今年8月，省农机发展中心在清徐县举办农机购置与应用补贴产品现场演示评价活动。本次活动旨在严格规范补贴产品准入标准，夯实农机购置补贴政策实施基础，推动先进适用农机装备的推广应用。参评产品涵盖农用（植保）无人驾驶航空器、农用北斗辅助驾驶系统、北斗农机作业监测终端、田园管理机及微耕机五大品类148个型号。\n\n“我们研发智能农机的最终目标是让农民成为‘白领’，坐在办公室就能种地。”闫太平说，智能农机的广泛落地、高效应用，离不开高素质专业人才的支撑。\n\n今年7月，省农业农村厅印发了《2026年全省高素质农民培育工作实施方案》。根据方案，培育工作专门面向参与智慧农业项目的新型农业经营主体骨干、青年农民骨干、返乡入乡创业群体等重点人群，紧扣我省智慧农业发展核心需求，重点围绕智慧农业发展理念、物联网、大数据、人工智能等信息技术应用、智能农业装备应用、智慧农（牧、渔）场发展等开展培训，破解智能农机“无人会用、无人善用”的人才短板。\n\n同时，全省各地也在积极探索适应智能农机操作的高素质农民培养新途径。朔州市举办的农机手技能提升培训班专门增设北斗导航辅助驾驶、作业质量监测等智能终端操作课程，帮助农机手适应现代农业发展需求。\n\n交口县组织技术人员将无人机驾驶技术培训直接送到田间地头，围绕农业无人机的核心应用场景，从设备构造、飞行原理、安全规范到植保作业参数设置，进行从理论到实操的全流程教学。乡宁县培育30名持证飞手，组建本土专业技术队伍，可熟练操控植保无人机开展梯田智能化作业。\n\n从单点设备示范突破，到全链条技术赋能，我省逐步探索出一条“科研攻关+政策托举+服务赋能”的智慧农业发展路径。曾经依赖人力、看天吃饭的传统农业，正被数据驱动、智能管控的现代化农业重塑。智能农机不再是大型示范基地的专属配置，而是持续下沉千家万户田块的普惠工具，持续释放农业新质生产力效能，为黄土高原旱作农业现代化、三晋乡村全面振兴注入源源不断的数字动能。\n\n![Image 1](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_0ab510f3-011f-4e35-afe4-86c9214ec13f_l.jpg)\n\n大棚专用自主水肥药农情监测机器人正在进行喷药作业。本报记者 崔如意摄\n\n![Image 2](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_2321f7b6-ed58-493b-9175-92dbb5561970_l.jpg)\n\n丘陵山区自走式马铃薯种植机正在作业。本报记者 崔如意摄\n\n**相关链接**\n\n《山西省智慧农业行动计划（2026—2030年）》提出，我省将：\n\n1.推进智慧农业先行先试。开展“人工智能+”行动，打造以新技术集群和大模型为支撑的大田种植、设施农业、规模养殖等应用场景，示范推广智能化农机装备。鼓励各市、县因地制宜打造1—2个种植业、畜牧业、渔业等智慧农业应用场景。鼓励建设智慧农业应用引领区，推进种植方案与物联网智能装备、智能灌溉系统、智能农机设备、智慧管理平台等耦合应用。用好线上线下宣传渠道，总结推广智慧农业发展经验做法。\n\n2.推进智慧农（牧、渔）场建设。实施农（牧、渔）场智慧赋能计划，培育一批不同技术模式的智慧农（牧、渔）场。发挥规模主体带动作用，培育一批智慧农业发展社会化服务主体，集成推广智慧农业新技术、新装备。\n\n3.推进智慧农业技术创新。支持科研院校开展智慧农业技术模式研究、集成与推广，利用农业生产过程的数字化仿真、推演，形成最优种植、养殖管理方案。加快智能农机装备技术攻关，加强丘陵山区智能农机装备研发、引进，开展丘陵山区杂粮农机具研究。推进国家级智慧农业重点实验室建设。组建省级智慧农业产业技术体系，为智慧农业发展提供技术支撑。培育一批智慧农业领域科技领军企业，推进智慧农业技术研发、成果转化和产业化应用。促进应用场景需求和研发服务能力对接，推动新技术新产品应用推广。","中国经济网扶贫频道 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":101,"substance":17,"depth":18,"authority":102,"freshness":55,"relevant":22,"comment":103},20,11,"省级智慧农业综合报道，智能农机研发与全链应用案例扎实、信源多元，但时效偏旧，适合作为主题聚合素材而非当日精选。",[105],{"name":97,"url":94},[27,29,30,107,108],"丘陵山区","农业社会化服务",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":112},{"candidates":113,"agricultural_evidence":46},[114,118,122,126],{"code":33,"score":19,"supported":46,"title":115,"abstract":116,"tags":117},[48],[48],[30],{"code":78,"score":55,"supported":46,"title":119,"abstract":120,"tags":121},[],[27],[27],{"code":54,"score":22,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[29],{"code":127,"score":22,"supported":14,"title":128,"abstract":129,"tags":130},"G2",[],[],[108],[132,133],"山西 智能农机 丘陵山区","山西 农业新质生产力 智慧农业","山西智能农机丘陵山区-5504",0,"2026-10-22T00:06:58.505233Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":8,"content":8,"source_name":142,"source_url":8,"published_at":8,"category":12,"cover_url":8,"hotness":13,"is_selected":46,"score":143,"score_detail":144,"sources":150,"tags":152,"primary_topic":155,"secondary_topics":156,"classification":157,"search_phrases":173,"slug":176,"view_count":177,"doi":8,"paper":8,"created_at":178},5117,"中国农科院南京农机所发布农机智能引擎AMES1000 破解智能农机底层核心技术瓶颈","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002F152411b343ff453d841275f2d2a3999b.htm","农业农村部南京农业机械化研究所发布拥有完全自主知识产权的农机智能引擎AMES1000新产品,破解我国智能农机底层核心技术瓶颈。南京农机所创新提出农机智能引擎AMES概念,联合中农国科(南京)智能装备科技有限公司完成产品落地,实现自动驾驶、处方图精准作业、多机协同、故障诊断等六大核心功能,具备高稳定、高可靠、易扩展等优势,可广泛适配各类农机机型,覆盖耕、种、管、收全农业生产场景,目前已在拖拉机、插秧机、田间管理机器人等装备上开展田间验证与熟化。","新华社 中国农业科学院 2026 年 10 月",89,{"impact":145,"substance":146,"depth":147,"authority":148,"freshness":21,"relevant":22,"comment":149},27,22,17,15,"国家级科研机构发布完全自主知识产权的农机智能引擎，破解智能农机底层核心技术瓶颈，属产业级重大突破，信息增量与权威度俱佳，值得进入每日精选。",[151],{"name":142,"url":140},[27,28,30,153,154],"自主可控","农机装备",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":158},{"candidates":159,"agricultural_evidence":46},[160,165,169],{"code":33,"score":161,"supported":46,"title":162,"abstract":163,"tags":164},19,[48],[48,50,51],[154,30],{"code":78,"score":22,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[27],{"code":60,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[28],[174,175],"南京农机所 AMES1000","农机智能引擎 AMES","南京农机所AMES1000-5117",2,"2026-10-17T00:13:39.688195Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":8,"content":184,"source_name":185,"source_url":8,"published_at":186,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":212,"slug":215,"view_count":66,"doi":8,"paper":8,"created_at":216},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":146,"substance":17,"depth":148,"authority":19,"freshness":89,"relevant":22,"comment":189},"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。",[191],{"name":185,"url":182},[27,28,30,193,194],"南南合作","中巴合作",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":198},{"candidates":199,"agricultural_evidence":46},[200,204,208],{"code":33,"score":55,"supported":46,"title":201,"abstract":202,"tags":203},[],[48],[30],{"code":78,"score":55,"supported":46,"title":205,"abstract":206,"tags":207},[],[27],[27],{"code":60,"score":22,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[28],[213,214],"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":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":8,"source_name":223,"source_url":220,"published_at":224,"category":225,"cover_url":8,"hotness":226,"is_selected":14,"score":227,"score_detail":228,"sources":231,"tags":236,"primary_topic":240,"secondary_topics":245,"classification":252,"search_phrases":298,"slug":301,"view_count":22,"doi":302,"paper":303,"created_at":315},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":146,"substance":101,"depth":17,"authority":229,"freshness":21,"relevant":22,"comment":230},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。",[232,233],{"name":223,"url":220},{"name":234,"url":235},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,28,29,237,238,239],"农业物联网","精准农业","自动驾驶农机",{"code":60,"slug":241,"name":242,"path":243,"parent_code":244},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[246,247],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":248,"slug":249,"name":250,"path":251,"parent_code":244},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":253},{"candidates":254,"agricultural_evidence":46},[255,264,270,277,284,288,294],{"code":60,"score":17,"supported":46,"title":256,"abstract":258,"tags":263},[257],"artificial intelligence",[259,260,261,257,262],"机器学习","计算机视觉","machine learning","computer vision",[28],{"code":33,"score":147,"supported":46,"title":265,"abstract":267,"tags":269},[266],"agricultural machinery",[50,268,266],"收割机",[],{"code":248,"score":13,"supported":46,"title":271,"abstract":272,"tags":276},[],[273,274,275],"物联网","internet of things","IoT",[237],{"code":278,"score":89,"supported":46,"title":279,"abstract":280,"tags":283},"I3",[],[281,282],"遥感","remote sensing",[],{"code":78,"score":86,"supported":46,"title":285,"abstract":286,"tags":287},[],[238],[27,238],{"code":289,"score":66,"supported":46,"title":290,"abstract":291,"tags":293},"F4",[],[292],"除草",[],{"code":54,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[29],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":223,"cited_by_count":135,"oa_url":220,"card":308,"direction":312,"ingested_from":314},"W7221138004",[306],{"name":307,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":309,"method":310,"finding":311,"direction":312,"opportunity":313},"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":317,"title":318,"url":319,"summary":320,"summary_zh":321,"content":8,"source_name":322,"source_url":319,"published_at":323,"category":225,"cover_url":8,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":327,"tags":329,"primary_topic":331,"secondary_topics":332,"classification":339,"search_phrases":363,"slug":366,"view_count":367,"doi":368,"paper":369,"created_at":393},3869,"Full-process mechanization of tea production: technological advances and prospects from mechanization-friendly planting to mechanical harvesting","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1963634","To address labor shortages and rising production costs in the tea industry, as well as the limited adaptability of machinery to hilly and mountainous tea plantations, this study systematically screened and synthesized the literature published between 1990 and 2026 following the PRISMA framework, ultimately including 216 studies. The results indicate that tea plantation mechanization is transitioning from stand-alone machinery and manual assistance toward lightweight, precision-based, intelligent, and fully coordinated operations. However, several challenges remain, including inadequate adaptability to complex terrain, insufficient integration of agricultural machinery with agronomic practices, limited reliability and standardization of key components, inadequate accuracy and efficiency in shoot and leaf recognition, localization, and low-damage harvesting, and an insufficient understanding of the relationship between mechanized operations and tea quality. Future research should promote the development of standardized, machinery-compatible tea plantations; develop modular operation platforms, high-mobility chassis, active terrain-following systems, and flexible end effectors; advance the application of multimodal sensing, autonomous navigation, digital twins, and multi-machine coordination; and establish long-term evaluation systems for operational performance and tea quality across regions and seasons. These efforts will help build an integrated, full-process mechanization system that closely coordinates tea plant characteristics, agronomic practices, mechanical equipment, and information-based control, thereby supporting the efficient, high-quality, green, and sustainable development of the tea industry.","为解决茶产业劳动力短缺、生产成本上升，以及机械对丘陵山区茶园适应性不足等问题，本研究遵循PRISMA框架，系统筛选并综合了1990年至2026年间发表的文献，最终纳入216项研究。结果表明，茶园机械化正从单机作业和人工辅助向轻量化、精准化、智能化和全程协同作业方向转型。然而，仍存在若干挑战，包括对复杂地形的适应性不足、农机与农艺融合不够、关键零部件可靠性和标准化程度有限、芽叶识别、定位和低损伤采摘的精度与效率不足，以及对机械化作业与茶叶品质之间关系的认识不充分。未来研究应推动标准化、宜机化茶园建设；开发模块化作业平台、高通过性底盘、主动仿形系统和柔性末端执行器；推进多模态感知、自主导航、数字孪生和多机协同的应用；并建立跨区域、跨季节的作业性能与茶叶品质长期评价体系。这些努力将有助于构建茶树特性、农艺措施、机械装备与信息化控制紧密协调的一体化全程机械化体系，从而支撑茶产业高效、优质、绿色和可持续发展。","Frontiers in Sustainable Food Systems","2026-09-30T00:00:00Z",80,{"impact":17,"substance":146,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":326},"基于216篇文献的系统综述，梳理茶园全程机械化技术路径与瓶颈，对丘陵山区智能农机研发有参考价值。",[328],{"name":322,"url":319},[27,29,30,107,330],"茶叶机械化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[333],{"code":334,"slug":335,"name":336,"path":337,"parent_code":338},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",{"status":40,"locked":14,"version":41,"method":42,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346,354,359],{"code":33,"score":13,"supported":46,"title":343,"abstract":344,"tags":345},[],[48,51,266],[30],{"code":334,"score":347,"supported":46,"title":348,"abstract":349,"tags":353},9,[],[350,351,352],"茶叶","茶树","tea plant",[],{"code":78,"score":55,"supported":46,"title":355,"abstract":356,"tags":358},[],[357],"数字孪生",[27],{"code":54,"score":22,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[29],[364,365],"茶园 全程机械化","茶叶 机械采摘 智能装备","茶园全程机械化-3869",7,"10.3389\u002Ffsufs.2026.1963634",{"doi":368,"openalex_id":370,"authors":371,"venue":322,"cited_by_count":135,"oa_url":319,"card":388,"direction":312,"ingested_from":314},"W7214907438",[372,374,376,379,382,385],{"name":373,"orcid":8},"Xingjian Yang",{"name":375,"orcid":8},"Yu Zhang",{"name":377,"orcid":378},"Yapeng Wu","https:\u002F\u002Forcid.org\u002F0009-0008-1808-8959",{"name":380,"orcid":381},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0009-0003-6743-0021",{"name":383,"orcid":384},"Xin Lu","https:\u002F\u002Forcid.org\u002F0009-0004-8662-3467",{"name":386,"orcid":387},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":389,"method":390,"finding":391,"direction":312,"opportunity":392},"系统综述1990-2026年216篇文献，梳理茶叶全程机械化技术进展与未来方向。","PRISMA框架系统筛选与综合216篇文献。","茶园机械化正转向轻量精准智能协同，但地形适应、农机农艺融合、嫩梢识别与低损采摘等仍存挑战。","可研究丘陵茶园多模态感知与自主导航采摘机器人，并建立跨区域季节的作业质量长期评价体系。","2026-10-01T23:30:06.812870Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":8,"source_name":400,"source_url":397,"published_at":401,"category":225,"cover_url":8,"hotness":13,"is_selected":46,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":410,"secondary_topics":411,"classification":412,"search_phrases":441,"slug":444,"view_count":55,"doi":445,"paper":446,"created_at":465},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy","2026-09-25T00:00:00Z",82,{"impact":146,"substance":404,"depth":17,"authority":229,"freshness":21,"relevant":22,"comment":405},21,"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[407],{"name":400,"url":397},[27,28,30,238,409],"智能播种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":413},{"candidates":414,"agricultural_evidence":46},[415,420,424,431,435],{"code":78,"score":21,"supported":46,"title":416,"abstract":417,"tags":419},[],[238,418],"precision agriculture",[27,238],{"code":33,"score":367,"supported":46,"title":421,"abstract":422,"tags":423},[],[48,266],[30],{"code":425,"score":89,"supported":46,"title":426,"abstract":427,"tags":430},"H2",[],[428,429],"土壤","soil",[],{"code":60,"score":55,"supported":46,"title":432,"abstract":433,"tags":434},[],[257],[28],{"code":436,"score":66,"supported":46,"title":437,"abstract":438,"tags":440},"B7",[],[439],"温室",[],[442,443],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":445,"openalex_id":447,"authors":448,"venue":400,"cited_by_count":135,"oa_url":397,"card":459,"direction":464,"ingested_from":314},"W7214297818",[449,451,452,454,456,458],{"name":450,"orcid":8},"Yuting Dong",{"name":377,"orcid":8},{"name":453,"orcid":8},"Shiguo Wang",{"name":455,"orcid":8},"Xiaohu Guo",{"name":383,"orcid":457},"https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":386,"orcid":387},{"tldr":460,"method":461,"finding":462,"direction":312,"opportunity":463},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","农业人工智能与决策模型","2026-09-26T23:30:47.821171Z"]