[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3002":3,"related-3002":71},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":40,"doi":8,"paper":60,"created_at":70},3002,"改进生物神经网络的农业播种机全覆盖路径规划","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1968","江苏大学魏军等提出一种考虑播种与非播种运动模式切换机制的改进生物神经网络（BNN）方法，基于周围环境条件将下一节点状态分类为播种、封闭或转移节点。在BNN景观引导下机器沿平行直线路径继续播种操作；检测到封闭节点时切换至非播种模式并使用深度优先搜索算法搜索潜在封闭区域；检测到转移节点时同样切换非播种模式搜索合理的新目标节点。仿真表明该方法实现播种操作的完全覆盖同时避免重复遍历已播种区域。",null,"MDPI Agriculture 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月","报道",89,{"impact":82,"substance":83,"depth":18,"authority":84,"freshness":85,"relevant":21,"comment":86},27,22,15,8,"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。",[88],{"name":78,"url":76},[26,27,28,90,91],"自主可控","农机装备",{"code":39,"slug":93,"name":94,"path":95,"parent_code":96},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[],{"status":99,"locked":13,"version":34,"method":35,"evidence":100},"classified",{"candidates":101,"agricultural_evidence":54},[102,110,114],{"code":39,"score":103,"supported":54,"title":104,"abstract":106,"tags":109},19,[105],"农机",[105,107,108],"农业机械","机械化",[91,28],{"code":45,"score":21,"supported":13,"title":111,"abstract":112,"tags":113},[],[],[26],{"code":50,"score":21,"supported":13,"title":115,"abstract":116,"tags":117},[],[],[27],[119,120],"南京农机所 AMES1000","农机智能引擎 AMES","南京农机所AMES1000-5117","2026-10-17T00:13:39.688195Z",{"id":124,"title":125,"url":126,"summary":127,"summary_zh":8,"content":128,"source_name":129,"source_url":8,"published_at":130,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":131,"score_detail":132,"sources":135,"tags":137,"primary_topic":140,"secondary_topics":141,"classification":142,"search_phrases":158,"slug":161,"view_count":162,"doi":8,"paper":8,"created_at":163},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":83,"substance":133,"depth":84,"authority":16,"freshness":20,"relevant":21,"comment":134},18,"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。",[136],{"name":129,"url":126},[26,27,28,138,139],"南南合作","中巴合作",{"code":39,"slug":93,"name":94,"path":95,"parent_code":96},[],{"status":99,"locked":13,"version":34,"method":35,"evidence":143},{"candidates":144,"agricultural_evidence":54},[145,150,154],{"code":39,"score":146,"supported":54,"title":147,"abstract":148,"tags":149},4,[],[105],[28],{"code":45,"score":146,"supported":54,"title":151,"abstract":152,"tags":153},[],[26],[26],{"code":50,"score":21,"supported":13,"title":155,"abstract":156,"tags":157},[],[],[27],[159,160],"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 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数字乡村","现代农事综合服务中心数字化-4705","2026-10-12T00:10:54.937448Z",{"id":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":8,"source_name":225,"source_url":222,"published_at":130,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":226,"score_detail":227,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":240,"classification":251,"search_phrases":285,"slug":288,"view_count":62,"doi":8,"paper":289,"created_at":307},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology",61,{"impact":16,"substance":228,"depth":84,"authority":16,"freshness":20,"relevant":21,"comment":229},16,"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[231],{"name":225,"url":222},[26,27,233,234,28],"精准施肥","农业物联网",{"code":236,"slug":237,"name":238,"path":239,"parent_code":96},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",[241,246],{"code":50,"slug":242,"name":243,"path":244,"parent_code":245},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":247,"slug":248,"name":249,"path":250,"parent_code":245},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":99,"locked":13,"version":34,"method":35,"evidence":252},{"candidates":253,"agricultural_evidence":54},[254,260,267,274,281],{"code":236,"score":84,"supported":54,"title":255,"abstract":257,"tags":259},[256],"fertilizer",[258,256],"肥料",[233],{"code":50,"score":12,"supported":54,"title":261,"abstract":262,"tags":266},[],[263,264,265],"机器学习","machine learning","artificial intelligence",[27],{"code":247,"score":12,"supported":54,"title":268,"abstract":269,"tags":273},[],[270,271,272],"物联网","internet of things","IoT",[234],{"code":45,"score":275,"supported":54,"title":276,"abstract":277,"tags":280},7,[],[278,279],"精准农业","precision agriculture",[26],{"code":39,"score":21,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[28],[286,287],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":8,"openalex_id":290,"authors":291,"venue":225,"cited_by_count":62,"oa_url":222,"card":300,"direction":304,"ingested_from":306},"W7221394563",[292,294,296,298],{"name":293,"orcid":8},"Anjali Manoj Kumbhar",{"name":295,"orcid":8},"Shruti Prashant Kulkarni",{"name":297,"orcid":8},"Snehal Santosh Khune",{"name":299,"orcid":8},"B. B. Godbole",{"tldr":301,"method":302,"finding":303,"direction":304,"opportunity":305},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","智慧农业 \u002F 农业物联网","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","openalex","2026-10-11T23:30:16.128775Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":8,"content":313,"source_name":314,"source_url":8,"published_at":171,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":315,"score_detail":316,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"classification":326,"search_phrases":343,"slug":346,"view_count":40,"doi":8,"paper":8,"created_at":347},4615,"杭州诚邀全市创新力量共筑智慧农业——启动人工智能+农业应用场景征集","https:\u002F\u002Fwww.hangzhou.gov.cn\u002Fcol\u002Fcol812262\u002Fart\u002F2026\u002Fart_61fd9b1bd08e4756a87e25e3f296e645.html","根据《杭州市大力发展智慧农业打造人工智能+农业创新实践高地实施方案(2026—2030年)》部署,杭州启动人工智能+农业应用场景征集,聚焦农业大模型和智能体、智能农业设施装备、智慧管理服务平台三大方向。场景要求具备合规性、创新性、实效性、示范性、开放性,优先支持向中小企业、民营企业开放测试应用合作;入选场景将有机会在第二十三届中国国际农产品交易会智慧农业展区专题展示。","杭州智慧农业正呼唤更多“最强大脑”——根据《杭州市大力发展智慧农业 打造“人工智能+农业”创新实践高地实施方案（2026—2030年）》部署，杭州日前已启动“人工智能+农业”应用场景征集，面向全市发出“英雄帖”。\n\n本次征集聚焦三大方向：农业大模型和智能体，包括生物育种、生产管理、病虫害监测、疫病防治、产量预测，以及病虫害智能诊断、农事决策支持、智能植保作业规划、农情问答、农业知识智能检索等；智能农业设施装备，包括智能农机、无人驾驶作业、智能采摘、精准施肥施药、水肥一体化智能控制、物联网感知、卫星遥感与无人机监测等；智慧管理服务平台，覆盖智慧种植、智慧畜牧、智慧渔业和综合管理服务，如植物工厂、智慧大田、智慧茶园果园、畜禽体征监测、精准饲喂、疫病AI辅助诊断、陆基工厂化养殖、质量安全溯源、乡村数字治理等。\n\n场景要“上榜”，需满足合规性、创新性、实效性、示范性和开放性。比如，场景建设要符合国家、省相关产业政策、安全规范和行业标准；依托人工智能核心技术，具备技术创新、模式创新或应用创新；已落地或具备明确落地计划，能解决行业痛点、治理难点、民生堵点，降本增效成效可量化、可验证；具备较强的复制性、推广性和通用性；优先支持向中小企业、民营企业开放测试、应用、合作的场景项目。\n\n征集主体须为在杭州市注册或主要应用场景在杭州的企事业单位、科研院所、高等院校、新型农业经营主体、农业社会化服务组织等。可单一报送，也可联合报送，联合报送须明确1家牵头单位，参与单位原则上不超过5家。有意向的主体可先向所在区、县（市）农业农村部门提交材料，经区、县（市）初审推荐，由市级组织专家开展路演，确定首批应用场景名单。","杭州市政府门户网站 2026-10-09",72,{"impact":228,"substance":317,"depth":84,"authority":19,"freshness":85,"relevant":21,"comment":318},20,"地市级政策落地动作，条款与征集方向具体，具备行业示范与可复制价值，值得进入每日精选。",[320],{"name":314,"url":311},[26,27,322,28,323],"农业大模型","应用场景征集",{"code":50,"slug":242,"name":243,"path":244,"parent_code":245},[],{"status":99,"locked":13,"version":34,"method":35,"evidence":327},{"candidates":328,"agricultural_evidence":54},[329,333,339],{"code":45,"score":16,"supported":54,"title":330,"abstract":331,"tags":332},[26],[26],[26],{"code":50,"score":334,"supported":54,"title":335,"abstract":336,"tags":338},5,[],[337],"大模型",[27,322],{"code":39,"score":21,"supported":13,"title":340,"abstract":341,"tags":342},[],[],[28],[344,345],"杭州 人工智能 应用场景征集","杭州 智慧农业 实施方案","杭州人工智能应用场景征集-4615","2026-10-11T00:09:35.817480Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":8,"content":8,"source_name":353,"source_url":8,"published_at":354,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":355,"score_detail":356,"sources":360,"tags":362,"primary_topic":364,"secondary_topics":365,"classification":366,"search_phrases":401,"slug":404,"view_count":275,"doi":8,"paper":8,"created_at":405},4124,"农民日报《对话》栏目「用算法种好地 让AI扛农活」——杜志雄、李想、孔德栋三位专家谈农业AI落地","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F30\u002F991030099.html","农民日报全媒体记者李克欣对话中国社科院杜志雄、中国农业大学李想、雄安集团孔德栋三位专家。专家指出当前我国智慧农业正从早期试点示范迈向规模化、体系化推广应用新阶段，人工智能赋能农业的关键技术包括机器学习与深度学习、计算机视觉与智能感知（如200万张高清图像训练的AI作物表型模型识别精度达95%以上）、农业大模型（如中化iMAP节约水肥10%~20%、增产8%~10%）和智能农机与农业机器人（如土壤高通量检测机器人实现42项指标检测误差控制在5%以内）。","中国农网（农民日报）·2026年9月30日","2026-09-30T00:00:00Z",83,{"impact":357,"substance":83,"depth":133,"authority":358,"freshness":334,"relevant":21,"comment":359},24,14,"央媒多方专家对话，含AI作物表型、农业大模型、农业机器人等具体数据与结论，信息增量足，但发布已过一周，时效性略降。",[361],{"name":353,"url":351},[26,27,322,363,28],"农业机器人",{"code":50,"slug":242,"name":243,"path":244,"parent_code":245},[],{"status":99,"locked":13,"version":34,"method":35,"evidence":367},{"candidates":368,"agricultural_evidence":54},[369,376,380,385,389,395],{"code":50,"score":370,"supported":54,"title":371,"abstract":372,"tags":375},11,[],[337,373,263,374],"深度学习","计算机视觉",[27,322],{"code":39,"score":146,"supported":54,"title":377,"abstract":378,"tags":379},[],[105],[28],{"code":381,"score":146,"supported":54,"title":382,"abstract":383,"tags":384},"F6",[],[363],[363],{"code":45,"score":146,"supported":54,"title":386,"abstract":387,"tags":388},[],[26],[26],{"code":390,"score":162,"supported":54,"title":391,"abstract":392,"tags":394},"H2",[],[393],"土壤",[],{"code":396,"score":162,"supported":54,"title":397,"abstract":398,"tags":400},"I3",[],[399],"作物表型",[],[402,403],"中化 iMAP 大模型","AI 作物表型 识别精度","中化iMAP大模型-4124","2026-10-04T00:09:05.162758Z"]