[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3002":3,"related-3002":46},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},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 16(18):1968","2026-09-14T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,21,17,13,6,1,"提出改进生物神经网络的全覆盖路径规划方法，方法新颖、结论可靠，但属细分领域学术进展，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","智能农机","路径规划","播种机",[32,33],"江苏大学 播种机 全覆盖路径规划","生物神经网络 播种机 路径规划","江苏大学播种机全覆盖路径规划-3002",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"提出改进生物神经网络，实现农业播种机全覆盖路径规划并避免重复播种。","改进BNN结合节点分类与深度优先搜索，仿真验证。","方法实现播种完全覆盖，同时避免重复遍历已播种区域。","农业人工智能与决策模型","可结合真实农田地形与多机协同，验证动态环境下的路径规划鲁棒性。","agent","2026-09-20T00:03:08.288198Z",{"total":20,"page":21,"page_size":20,"items":47},[48,74,126,155,178,207],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":8,"content":53,"source_name":54,"source_url":8,"published_at":55,"category":56,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":57,"sources":63,"tags":65,"search_phrases":69,"slug":72,"view_count":35,"doi":8,"paper":8,"created_at":73},2898,"苏垦农发神农慧种农业AI大模型规模化落地:天空地一体化闭环,百万亩自有农田实景数据","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260918101757264727920","苏垦农发9月18日发文,公司依托百万亩自有连片高标准农田,持续产出真实大田数据训练神农慧种农业AI智能体,实现天空地一体化数据闭环:空中多光谱无人机集群常态化农田巡测;地面全域四情监测传感器、北斗智能农机、智能灌溉终端;云端苏垦智云平台+神农慧种AI模型,形成采集数据→AI分析研判→输出水肥植保方案→农机落地执行完整闭环。苏垦智云是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。","[在东方财富看资讯行情，选东方财富证券一站式开户交易>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n（国内A股找不到第二家，像苏垦农发依托百万亩自有连片高标准农田，持续产出真实大田数据训练神农慧种农业AI智能体；苏垦实现天空地一体化数据闭环，苏垦智云是农林牧渔唯一工信部信创典型案例，智慧农业+低空经济双主线落地。）\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n- 云端：苏垦智云平台与神农慧种AI模型，形成「采集数据→AI分析研判→输出水肥植保方案→农机落地执行」完整闭环。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n百万亩自有连片农田源源不断产出真实田间数据，持续迭代优化AI模型。国内很多农业AI企业仅拥有小片试验田，唯有苏垦农发拥有大规模现代农业实景数据用于农业模型训练。\n\n苏垦智云平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。苏垦农发一一智慧农业与低空经济的天空地一体化闭环落地，AI大田规模化实体应用！\n\n苏垦农发打造天空地一体化智慧农业完整体系，AI大田并非实验室试验，而是在百万亩自有连片农田实现规模化落地运行。\n\n1、空中：多光谱无人机集群常态化农田巡测，低空遥感采集作物长势、病虫害、墒情数据；依托农业农村部低空技术创新重点实验室，主攻低空多模态农情感知。\n\n2、地面：全域农田“四情”监测传感器、北斗智能农机、智能灌溉终端，实时采集土壤、苗情、虫情、气象数据。\n\n3、云端：苏垦智云平台 神农慧种农业AI智能体，形成完整闭环：采集田间数据→AI模型分析研判→输出水肥、植保作业处方→下发农机执行落地。\n\n4、核心稀缺壁垒：手握百万亩自有连片高标准农田，源源不断产出真实大田实景数据，持续迭代训练神农慧种AI模型。\n\n国内绝大多数农业AI企业，仅拥有小片试验田做演示；苏垦是少数拥有大规模真实农业场景用于模型训练与生产验证的实体龙头。\n\n5、苏垦智云一体化平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台，国产化底层架构，是农业数字化可复制的标杆样板。配套全国首个农业农村部农业低空技术创新重点实验室（苏垦为依托单位、河海大学共建），同步布局低空经济与智慧农业新质生产力。\n\n2026-09-18 11:16:07 作者更新了以下内容\n\n全球领先的风险咨询公司Verisk Maplecroft 在周四（9月17日）发布的一份报告中表示，极端天气灾害将加剧亚洲的粮食安全风险，并可能在印度、印尼和菲律宾等脆弱的国家引发动荡。\n\n![Image 4](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9ACF9C92E7B35A242970CDC4D55B8DA9_w1080h15645.jpg)\n\n![Image 5](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FFE1BF075831D4DAEB94ADAE924123EE9_w1080h2400.jpg)\n\n2026-09-18 21:02:06 作者更新了以下内容\n\n苏垦农发一一 AI赋能农业真实落地案例：临海农场——国内首个10万亩级无人值守巡田农场（核心标杆）\n\n地点：江苏盐城临海农场，苏垦智慧农业科技园\n\n1. 空中低空遥感AI巡田\n\n![Image 6](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FE4E4DDE3ED1EEF061A2D631622A46F73_w1424h800.jpg)\n\n多光谱无人机集群常态化巡航，采集苗情、墒情、病虫害影像数据，AI自动识别长势差异、病斑，生成热力图；替代人工徒步巡田，十几分钟就能完成万亩农田普查。依托农业农村部低空技术创新重点实验室，开展低空多模态农情感知研究。\n\n2. AI智能光伏远程灌溉系统\n\n![Image 7](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9FF2DAAEBED53D7BB8C825B388C102BD_w1424h800.jpg)\n\n万亩稻田布设太阳能智能闸门，通过土壤墒情传感器采集数据，AI分析土壤缺水程度，手机APP一键远程开关水渠闸门。\n\n量化效果：过去管500亩农田，人工开关闸门半天；现在2分钟完成全部闸门调控，灌溉效率提升20倍，每亩节约管水人工成本约30元。\n\n3. AR眼镜AI虫害识别\n\n![Image 8](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9C7DFBF3F14BDE7E73309CA953C0C8E7_w1424h800.jpg)\n\n农技人员佩戴AR眼镜在田间巡查，拍摄虫体，AI毫秒级识别稻飞虱等害虫种类、统计虫口密度，识别准确率＞95%，自动推送防治方案，新手农技员也能快速判别田间虫害。\n\n4. AI变量施肥无人机作业\n\n![Image 9](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FC257CCA1B1E8AE1621C403E96DF222EE_w1424h800.jpg)\n\nAI读取水稻营养、长势数据，为每一块条田生成独立追肥处方，无人机分区精准施肥，一地一策，实现肥药双减，农药化肥年均用量下降约3%。\n\n5. 北斗智能农机 AI收割决策\n\n![Image 10](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F00A2AE87C3E170BFBFE610FABE2556A5_w1424h800.jpg)\n\n北斗导航插秧机、无人收割机，AI根据成熟度、含水率数据，指导分块错峰收割，减少粮食收割损耗。\n\n[恭喜解锁12个月手机L2专属领取资格，立即领取>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n暗盘资金榜已更新!这些个股\u002F板块可以关注>\n\n追加内容\n\n本文作者可以追加内容哦 !\n\n**郑重声明：**用户在社区发表的所有信息将由本网站记录保存，仅代表作者个人观点，与本网站立场无关，不对您构成任何投资建议，据此操作风险自担。**请勿相信代客理财、免费荐股和炒股培训等宣传内容，远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、微信及QQ等信息，谨防上当受骗！**\n\n[![Image 11](https:\u002F\u002Favator.eastmoney.com\u002Fqface\u002F9825094237066000\u002F360)](https:\u002F\u002Fi.eastmoney.com\u002F9825094237066000)\n\n总收益 20日收益 日收益\n------\n\n历史收益率走势(%)\n\nChart\n\n代码 名称 最新价 涨跌幅\n[查看更多](http:\u002F\u002Figuba.eastmoney.com\u002F9825094237066000)\n\n浪客视频\n\n![Image 12](https:\u002F\u002Fnp-newspic.dfcfw.com\u002Fdownload\u002FD25261481966621695940_w340h340.jpg)\n\n![Image 13](https:\u002F\u002Fgbapi.eastmoney.com\u002Fshareopt\u002Fweb\u002Fweb_click.gif?id=20260918101757264727920&type=20&version=200&product=EastMoney&plat=Web&deviceid=caifuhao)\n\n郑重声明：东方财富网发布此信息的目的在于传播更多信息，与本站立场无关。东方财富网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资建议，据此操作，风险自担。","东方财富财富号\u002F苏垦农发","2026-09-18T00:00:00Z","报道",{"impact":58,"substance":59,"depth":60,"authority":61,"freshness":12,"relevant":21,"comment":62},22,18,14,5,"苏垦农发百万亩自有农田上实现天空地一体化AI闭环，含临海农场10万亩无人巡田等量化案例，产业参考价值较高，但来源为财富号自媒体、宣传色彩浓，权威性偏弱。",[64],{"name":54,"url":51},[26,66,27,28,67,68],"低空经济","遥感监测","数字农田",[70,71],"苏垦农发 神农慧种 AI大模型","临海农场 无人值守巡田","苏垦农发神农慧种AI大模型-2898","2026-09-19T00:06:07.612319Z",{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":8,"source_name":80,"source_url":77,"published_at":81,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":82,"score_detail":83,"sources":87,"tags":89,"search_phrases":92,"slug":95,"view_count":35,"doi":96,"paper":97,"created_at":125},2616,"Multi-agent cooperative control for unmanned distributed-drive electric agricultural vehicle in paddy fields: tracking, stability, and energy-aware torque allocation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112422","With the rapid advancement of intelligent agriculture and autonomous field operations, the distributed drive electric plant protection vehicle (DDEPPV) is increasingly adopted for paddy-field plant protection. However, in soft-soil, low-adhesion, and highly disturbed environments, the tight coupling among path tracking, drive\u002Fyaw stability, and energy consumption-together with uncertain ground parameters-poses major challenges to conventional control. In addition, from-scratch reinforcement learning is difficult to deploy, as early exploration can induce yaw instability and wheel entrapment. To overcome these limitations, we propose a vehicle-level distributed electric-drive control framework that integrates physics-informed priors with multi-agent cooperative learning. A mud-water multiphase wheel-soil interaction model is built via CFD-DEM coupling to identify, under the parameter settings and operating conditions considered in this study, an energy- and sinkage-risk-aware slip-ratio window, thereby providing an interpretable ground-mechanics boundary for subsequent controller design. Under the centralized training and decentralized execution paradigm, the task is decomposed into three agents for path tracking, stability\u002Ftraction regulation, and energy-optimal four-wheel allocation, and trained using model predictive control (MPC) expert-supervised pretraining followed by multi-agent twin delayed deep deterministic policy gradient (MATD3) cooperative fine-tuning. Real-time Hardware-in-the-Loop (HIL) experiments verify improved turning performance and enhanced yaw\u002Ftraction stability, while reducing traction-system electrical energy consumption by 29.4% versus MPC and by an additional 5.4% over unpretrained MATD3, demonstrating unified optimization of accuracy-stability-energy efficiency in paddy fields.","随着智能农业与自主田间作业的快速发展，分布式驱动电动植保车辆（DDEPPV）在水稻田植保作业中得到日益广泛的应用。然而，在软土、低附着力和高扰动环境中，路径跟踪、驱动\u002F偏航稳定性与能耗之间的紧密耦合，加之地面参数的不确定性，给传统控制带来了重大挑战。此外，从零开始的强化学习难以部署，因为早期探索可能引发偏航失稳和车轮陷坑。为克服这些局限，我们提出了一种车辆级分布式电驱动控制框架，将物理信息先验与多智能体协同学习相融合。通过CFD-DEM耦合建立了泥水多相轮-土相互作用模型，在本研究所考虑的参数设置和作业条件下识别出兼顾能耗与下陷风险的滑转率窗口，从而为后续控制器设计提供可解释的地面力学边界。在集中训练-分散执行范式下，将任务分解为路径跟踪、稳定性\u002F牵引力调节和能耗最优四轮分配三个智能体，并采用模型预测控制（MPC）专家监督预训练，随后通过多智能体双延迟深度确定性策略梯度（MATD3）进行协同微调。实时硬件在环（HIL）实验验证了转向性能的改善以及偏航\u002F牵引稳定性的增强，同时牵引系统电能消耗较MPC降低29.4%，较未经预训练的MATD3进一步降低5.4%，展示了水稻田中精度-稳定性-能效的统一优化。","Computers and Electronics in Agriculture","2026-09-15T00:00:00Z",82,{"impact":59,"substance":58,"depth":84,"authority":60,"freshness":85,"relevant":21,"comment":86},19,9,"提出物理先验与多智能体协同学习融合的分布式驱动电动农机控制框架，HIL实验验证能耗降低29.4%，方法新颖、数据扎实，对水田智能装备研发有实质参考价值。",[88],{"name":80,"url":77},[26,27,28,90,91],"水稻生产","多智能体控制",[93,94],"农业人工智能 多智能体控制 智慧农业 智能农机","农业人工智能 多智能体控制","农业人工智能多智能体控制智慧农业智能农机-2616","10.1016\u002Fj.compag.2026.112422",{"doi":96,"openalex_id":98,"authors":99,"venue":80,"cited_by_count":35,"oa_url":77,"card":119,"direction":42,"ingested_from":124},"W7213225972",[100,103,105,107,110,112,114,116],{"name":101,"orcid":102},"Wenxiang Xu","https:\u002F\u002Forcid.org\u002F0000-0001-6476-4710",{"name":104,"orcid":8},"Xiaoyu Song",{"name":106,"orcid":8},"Liling Ye",{"name":108,"orcid":109},"Mengnan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5418-6347",{"name":111,"orcid":8},"He Zheng",{"name":113,"orcid":8},"Mingfeng Wang",{"name":115,"orcid":8},"Ze Liu",{"name":117,"orcid":118},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"tldr":120,"method":121,"finding":122,"direction":42,"opportunity":123},"提出多智能体协同控制框架，实现水田分布式驱动电动农机路径跟踪、稳定性与能耗统一优化。","CFD-DEM泥水轮土模型、MPC专家预训练、MATD3多智能体协同微调、HIL","相比MPC降低牵引电耗29.4%，比未预训练MATD3再降5.4%，提升转向与横摆稳定性。","可探索将物理先验与多智能体强化学习迁移至其他软土农田作业场景，并降低对高保真仿真模型的依赖。","openalex","2026-09-16T23:30:01.996916Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":8,"content":8,"source_name":131,"source_url":8,"published_at":132,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":133,"score_detail":134,"sources":137,"tags":139,"search_phrases":143,"slug":146,"view_count":35,"doi":8,"paper":147,"created_at":154},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":135,"substance":58,"depth":59,"authority":60,"freshness":20,"relevant":21,"comment":136},15,"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。",[138],{"name":131,"url":129},[26,27,28,140,141,142],"大语言模型","农机农艺融合","白萝卜",[144,145],"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",{"doi":8,"openalex_id":8,"authors":148,"venue":8,"cited_by_count":35,"oa_url":8,"card":149,"direction":42,"ingested_from":44},[],{"tldr":150,"method":151,"finding":152,"direction":42,"opportunity":153},"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。","2026-09-15T00:04:27.326828Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":8,"content":160,"source_name":161,"source_url":8,"published_at":162,"category":56,"cover_url":8,"hotness":12,"is_selected":13,"score":163,"score_detail":164,"sources":168,"tags":170,"search_phrases":173,"slug":176,"view_count":35,"doi":8,"paper":8,"created_at":177},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。","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 2026-09-15","2026-09-14T16:00:00Z",68,{"impact":59,"substance":165,"depth":16,"authority":60,"freshness":166,"relevant":21,"comment":167},16,8,"农业农村部农机化总站主办的全国性智能农机培训通知，课程覆盖农业AI、农业芯片、传感器、北斗导航与典型场景，具备行业指导价值，但属会议培训类事务性通知，信息增量有限。",[169],{"name":161,"url":158},[26,27,171,28,172],"农业机器人","农机培训",[174,175],"农业人工智能 农业机器人 农机培训 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农机培训智慧农业-2446","2026-09-15T00:04:20.009285Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":8,"content":8,"source_name":183,"source_url":8,"published_at":184,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":185,"score_detail":186,"sources":189,"tags":191,"search_phrases":194,"slug":197,"view_count":35,"doi":8,"paper":198,"created_at":206},2214,"中国农业大学信电学院余强教授团队:面向田间工况的电动拖拉机混合储能系统分层自适应能量管理策略","https:\u002F\u002Fciee.cau.edu.cn\u002Fart\u002F2026\u002F9\u002F8\u002Fart_50389_1135985.html","中国农业大学为该论文唯一完成单位,余强教授为论文通讯作者,2022级博士研究生何雄林为第一作者。论文提出运行信息融合(OIF)方法,通过主成分分析、K-means聚类和概率神经网络实现田间作业工况的在线识别,并构建分层自适应能量管理策略。运行模式识别在线准确率达97.2%,速度和牵引阻力预测精度分别提高7.2%-12.9%;与传统模型预测控制相比,所提策略使动力电池最终荷电状态提高12.0%,系统总运行成本降低17.2%,电池退化成本降低16.7%。","中国农业大学信息与电气工程学院 2026年9月8日","2026-09-08T00:00:00Z",79,{"impact":59,"substance":187,"depth":59,"authority":60,"freshness":20,"relevant":21,"comment":188},23,"中国农大团队提出面向田间工况的电动拖拉机分层自适应能量管理策略，方法新颖、数据详实，对智能农机电动化具有参考价值。",[190],{"name":183,"url":181},[26,27,28,192,193],"电动拖拉机","能量管理",[195,196],"农业人工智能 电动拖拉机 智慧农业 智能农机","农业人工智能 电动拖拉机","农业人工智能电动拖拉机智慧农业智能农机-2214",{"doi":8,"openalex_id":8,"authors":199,"venue":8,"cited_by_count":35,"oa_url":8,"card":200,"direction":204,"ingested_from":44},[],{"tldr":201,"method":202,"finding":203,"direction":204,"opportunity":205},"提出面向田间工况的电动拖拉机混合储能分层自适应能量管理策略。","主成分分析、K-means聚类与概率神经网络在线识别工况，结合分层自适应策略。","工况识别准确率97.2%，电池荷电状态提高12.0%，总运行成本降低17.2%。","农业绿色发展与碳","可探索多机协同与真实农田复杂工况下的能量管理泛化能力及碳减排量化。","2026-09-12T00:06:40.429924Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":8,"source_name":80,"source_url":210,"published_at":213,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":214,"score_detail":215,"sources":218,"tags":220,"search_phrases":223,"slug":226,"view_count":35,"doi":227,"paper":228,"created_at":251},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》","2026-09-04T00:00:00Z",66,{"impact":135,"substance":59,"depth":165,"authority":135,"freshness":216,"relevant":21,"comment":217},2,"论文提出基于无人机影像的联合收割机路径规划方法，属于细分领域进展，但时效性较低。",[219],{"name":80,"url":210},[26,221,27,29,222],"无人机","农机装备",[224,225],"农业人工智能 农机装备 智慧农业 路径规划","农业人工智能 农机装备","农业人工智能农机装备智慧农业路径规划-1711","10.1016\u002Fj.compag.2026.112382",{"doi":227,"openalex_id":229,"authors":230,"venue":80,"cited_by_count":35,"oa_url":8,"card":245,"direction":249,"ingested_from":124},"W7208720259",[231,234,236,238,240,242],{"name":232,"orcid":233},"Hongbo Jia","https:\u002F\u002Forcid.org\u002F0000-0001-5426-347X",{"name":235,"orcid":8},"Jun Zhou",{"name":237,"orcid":8},"Jingwei Sun",{"name":239,"orcid":8},"Licun Yuan",{"name":241,"orcid":8},"Yongqiang He",{"name":243,"orcid":244},"Muhammad Aurangzaib","https:\u002F\u002Forcid.org\u002F0000-0002-4445-8801",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"基于无人机影像为无人联合收割机规划边缘作业路径。","利用无人机影像生成地图，结合路径规划算法。","实现了无人收割机边缘作业的自动路径规划。","智慧农业 \u002F 农业物联网","可探索动态障碍物下的实时路径重规划，或结合多机协同作业优化。","2026-09-05T23:30:02.064293Z"]