[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2898":3,"related-2898":38},{"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":23,"tags":25,"search_phrases":32,"slug":35,"view_count":36,"doi":8,"paper":8,"created_at":37},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。",null,"[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",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},22,18,14,5,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","低空经济","农业人工智能","智能农机","遥感监测","数字农田",[33,34],"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",0,"2026-09-19T00:06:07.612319Z",{"total":39,"page":21,"page_size":39,"items":40},6,[41,66,90,144,198,246],{"id":42,"title":43,"url":44,"summary":45,"summary_zh":8,"content":46,"source_name":47,"source_url":8,"published_at":48,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":49,"score_detail":50,"sources":56,"tags":58,"search_phrases":61,"slug":64,"view_count":21,"doi":8,"paper":8,"created_at":65},1363,"北大荒信息有限公司：寒地作物大模型与离朱·智能遥感平台接入 49 颗卫星，垦区每 5 天更新作物长势监测","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1256a96d87c79652","北大荒信息有限公司已为 4800 余万亩耕地建立数字化档案，研发各类数字化系统 60 余个。寒地作物大模型综合土壤、气象、作物长势信息辅助生成种植方案和变量施肥处方。自主研发的离朱·智能遥感平台接入 49 颗卫星，每 5 天即可完成一轮作物长势监测；垦区田间布设近 5000 台数据采集设备。","新华社哈尔滨9月1日电**题：“黑土粮仓”焕新记**\n\n新华社记者沈易瑾、王优玲、黄腾\n\n从“看天吃饭”到“看屏种地”，从“经验育种”到“精准选种”，从“机械作业”到“智能操控”……新华社记者随“活力中国调研行”采访团在黑龙江走访发现，数字技术、种业创新、智能装备加速走进黑土地，农业生产方式正不断焕新。\n\n走进北大荒信息有限公司，覆盖千万亩耕地的“数字地图”铺满大屏。地块信息、土壤墒情、作物长势、农机轨迹等数据不断更新，卫星遥感、物联网、人工智能等技术逐步融入耕、种、管、收各环节。\n\n北大荒信息有限公司市场运营中心副总经理王浩介绍，目前，公司已为4800余万亩耕地建立数字化档案，研发各类数字化系统60余个。依托长期积累的农业数据，企业研发的寒地作物大模型能够综合土壤、气象、作物长势等信息，辅助生成种植方案和变量施肥处方。\n\n“过去人工巡田，一个人一天大约只能查看200亩地。现在借助无人机、遥感等技术，可以开展大范围巡田，基本每5天就能更新一次垦区作物长势监测结果。”王浩说，数据正成为巡田、施肥、植保等农事决策的重要依据。\n\n从“靠经验”到“看数据”，田间管理越来越精准；从种源端发力，粮食增产潜力也在进一步释放。黑龙江省绥化市北林区的盛昌种子繁育有限责任公司水稻种植基地里，稻穗飘香，稻浪滚滚。技术员卢国臣俯身查看秧苗，边记录边说：“今年新引进的品种长势不错，现在已经进入蜡熟期。”\n\n基地对面，崖州湾国家实验室（绥化）粮油作物创新平台实验室正加紧建设；示范田内，一处新建的抗寒实验设备已经投入使用。\n\n“新引进的种子试种前都要进行抗寒测试。”公司总经理王会说，种子需在恒温15摄氏度的水中持续浸泡，耐寒性达到当地种植要求后，才能进入下一步研发和繁育。\n\n王会介绍，今年5月，绥化市与崖州湾国家实验室签约共建绥化粮油作物创新平台，盛昌种业流转500亩耕地作为试验田，承接南繁北育科研任务，分子育种、基因编辑等前沿技术将应用于寒地作物培育，为新品种选育提供技术支撑。\n\n“好种子”还要“种得好”，新品种不断迭代，农机装备也在向“智”升级。\n\n在哈尔滨市双城区，黑龙江德沃科技开发有限公司，一台台电驱气力式精密播种机整齐排列。风压是否稳定、有没有漏播重播、株距设置多少……这些过去更多依靠机械结构和人工调节的环节，如今可以通过智能终端实时监测和设置。\n\n“电驱系统让作业参数调整更加便捷，排种方式也由机械夹种改为气力吸种，减少种子损伤。”黑龙江德沃科技开发有限公司总工程师杜木军介绍，这款播种机作业速度可达每小时8至12公里，相比传统机械式播种机提高约50%，还可同步完成侧深施肥、覆土镇压等工序。\n\n播得快，还要播得准。企业电驱排种试验室里，每天要开展上百次排种试验。研发人员逐次分析测试数据，再根据试验数据反复调整排种器、变速箱等关键部件。\n\n![Image 1](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3647446424-C803E5C542109517A24A455AD56CA02F\u002F0?fmt=jpg&size=176&h=768&w=1024&ppv=1)\n\n德沃科技研发的电驱气力式精密播种机（9月1日摄）。新华社记者沈易瑾 摄\n\n“目前，我们的设备在国内高端电驱播种机销量中占比超过60%。”杜木军说，近年来，企业围绕马铃薯全程机械化装备、秸秆处理装备等领域持续研发，更好适应规模化、精准化农业生产需求。\n\n作为我国产粮第一大省，黑龙江粮食总产量已连续16年位居全国首位，全省农作物耕种收综合机械化率达到99.28%。科技创新加快融入粮食生产各环节，持续挖掘粮食稳产增产潜力，为“黑土粮仓”注入新的发展动能。","新华社 · 2026-09-01","2026-09-01T01:00:00Z",82,{"impact":51,"substance":52,"depth":53,"authority":53,"freshness":54,"relevant":21,"comment":55},24,20,15,8,"央媒报道北大荒寒地作物大模型与遥感平台应用，覆盖4800万亩耕地，每5天更新长势监测，兼具产业影响与信息增量。",[57],{"name":47,"url":44},[26,28,29,59,60,30],"种业振兴","北大荒",[62,63],"农业人工智能 智慧农业 智能农机 种业振兴","农业人工智能 智慧农业","农业人工智能智慧农业智能农机种业振兴-1363","2026-09-02T00:05:05.669960Z",{"id":67,"title":68,"url":69,"summary":70,"summary_zh":8,"content":71,"source_name":72,"source_url":8,"published_at":73,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":80,"tags":82,"search_phrases":85,"slug":88,"view_count":36,"doi":8,"paper":8,"created_at":89},2849,"苏垦农发AI赋能农业第一股:神农慧种农业垂直智能体落地百万亩农田","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260917140319909504910","苏垦农发南京实验室2026上半年正式运营,9月9日发布五大开放课题,主攻低空感知、农业AI认知模型,属于国内农业低空领域最高级别科研载体之一。神农慧种农业大模型由南京农业大学牵头,苏垦农发、江苏省农科院联合共建,覆盖稻、麦、玉、豆四大粮油作物,可实现种质辅助育种、作物长势识别、病虫害智能研判、高温热害预警、水肥AI处方推荐。该系统已在临海、黄海、江心沙等分公司常态化投入使用,成效明确:病虫害识别准确率>90%,巡田效率提升3-5倍,依托AI方案实现氮肥减量5%-15%。","[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苏垦农发是A股百万亩实景大田AI农业龙头，拥有全国首个国家级农业低空重点实验室，无人机巡田、四情物联网、神农慧种农业大模型落地连片农田，AI技术直接用于稻麦增产。\n\n公司主业国内粮食种植，不受美联储加息直接冲击，兼具粮食安全防御+智慧农业新质生产力双重逻辑。\n\n一、国家级科研平台加持\n\n公司与农业农村部、河海大学共同建设农业农村部农业低空技术创新重点实验室，也是国内首个农业低空领域重点实验室，主攻低空多模态农情感知、农业AI认知模型、空地一体化智慧农业体系 。\n\n9月9日实验室发布五大课题，主攻田间AI识别、低空遥感。\n\n二、百万亩真实大田作为AI试验场（最大差异化优势）\n\n公司自有近百万亩连片高标准农田，配套“四情传感器”、多光谱巡田无人机、远程智能灌溉、北斗农机，AI不是停留在实验室，直接落地在自家稻麦生产。\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260917\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260917\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n无人机多光谱扫描识别长势、病虫害，AI模型预测产量，手机APP远程管控灌溉，实现“平板管田、屏幕知墒”。苏垦智云平台入选工信部信创典型案例 。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260917\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n三、农业大模型落地\n\n公司联合南京农业大学、江苏省农科院推出神农慧种农业智能体，配套田间智能体“苏小润”，覆盖稻麦全生长周期，用田间真实农田数据训练农业专用大模型，用于种植预测、农事决策 。\n\n四、业务模式\n\n苏垦农发利用AI技术降本增效、提升粮食产量，服务主业稻麦种植；\n\n同时向外输出智慧农业农服方案，AI属于赋能工具，AI业务目前不单独产生独立营收，业绩根基还是粮食种植。\n\n2026-09-17 14:25:55 作者更新了以下内容\n\n苏垦农发——具有三大核心优势的AI科技实力位居农业板块第一梯队！\n\n苏垦农发并是A股稀缺手握百万亩真实大田场景、国家级科研平台，并且AI 低空遥感技术已经规模化落地的现代农业实体龙头。\n\n一、AI科技三大核心优势\n\n1. 国家级平台背书：全国首个农业农村部低空技术创新重点实验室\n\n苏垦作为依托单位，联合河海大学共建。实验室2026上半年正式运营，9月9日发布五大开放课题，主攻低空感知 农业AI认知模型，属于国内农业低空领域最高级别科研载体之一。\n\n五大攻关方向：低空多模态农情感知、低空数据驱动的农业AI认知模型、空地一体化巡检协同系统、无人机云网调度平台、农田空间智能应用。\n\n苏垦提供百万亩连片高标准农田，作为AI算法、无人机、传感器的实景试验场，这是绝大多数农业AI企业不具备的核心壁垒。\n\n2. 神农慧种农业垂直智能体落地，产学研协同攻关\n\n神农慧种农业大模型由南京农业大学牵头，苏垦农发、江苏省农科院联合共建。苏垦提供多年大田种植、育种海量真实田间数据，用于训练农业垂直大模型。\n\n覆盖稻、麦、玉、豆四大粮油作物，可实现种质辅助育种、作物长势识别、病虫害智能研判、高温热害预警、水肥AI处方推荐，从问答式大模型升级为直接指导田间作业的农业生产智能体。\n\n该系统已在临海、黄海、江心沙等分公司常态化投入使用，成效明确：病虫害识别准确率＞90%，巡田效率提升3-5倍，依托AI方案实现氮肥减量5%-15%，真正做到降本增效。\n\n3.智慧农业与低空经济的天空地一体化闭环落地，AI大田应用规模化跑通\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n- 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。\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。很多农业AI企业仅拥有小片试验田，缺少大规模实景数据用于模型训练。\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。\n\n二、AI科技实力横向对比定位\n\n1. 对比粮食种植上市公司（北大荒、金健米业等）\n\n苏垦AI科技实力断层领先，国内A股唯一同时拥有国家级低空重点实验室 百万亩大田AI落地场景的标的。\n\n2. 对比种业上市公司（敦煌种业、万向德农）\n\n苏垦农发的AI育种、大田数字化管理能力显著更强，数字化管理覆盖从育种到大田种植全链条。\n\n2026-09-17 21:40:04 作者更新了以下内容\n\n全球粮价持续上行！\n\n当前全球粮食安全已进入新一轮紧平衡，2026年全球粮价中枢或持续上行。核心催化因素有三：\n\n一是超强厄尔尼诺，NOAA数据显示2026年秋冬出现\"非常强厄尔尼诺\"的概率超90%，可能扰动东南亚、印度、澳洲及南美粮食主产区；\n\n二是地缘与能源成本推升农产品价格；\n\n三是国内种业振兴政策持续落地，转基因商业化加速。\n\n哪些板块业绩弹性最大？\n\n一、种业板块：弹性最高的\"粮食芯片\"\n\n种业被视为粮食产业链的\"芯片\"，具有最高业绩弹性，主要受益于极端天气带来的抗逆品种需求提升、粮价上行带动的农户购种意愿增强，以及生物育种商业化和种业知识产权政策的推动。\n\n核心标的财务数据（2026中报）：\n\n公司 归母净利润 营收同比 毛利率 机构预测亮点\n\n敦煌种业 1.447亿元 20.93% 48.61% 种子业务营收7.75亿，同比 35.20%；净利润2.98亿，同比 102.49%\n\n苏垦农发 1.44亿元 -5.594% 11.31% 机构预测2026全年归母净利润7.3亿，维持\"跑赢行业\"评级 ，水稻、小麦、玉米种子龙头\n\n登海种业 4952万元 26.96% 30.36% 玉米种子龙头，转基因品种完成审定，\n\n农发种业 5255万元 32.84% 5.453% 央企种业平台，种子 农资协同\n\n隆平高科 -2.727亿元 -21.05% 39.05% 机构预测2026全年归母净利润3.39亿，同比 104.6%\n\n万向德农 1380万元 -15.14% 26.48% 转基因概念，年初至今涨幅 81.65%\n\n二、种植\u002F农垦板块：直接受益但弹性弱于种业\n\n拥有大规模土地资源的粮食种植与农垦板块直接受益粮价上行，但价格弹性弱于种业。\n\n公司 归母净利润 营收同比 毛利率 机构预测亮点\n\n苏垦农发 1.44亿元 -5.594% 11.31% 机构预测2026全年归母净利润7.3亿，维持\"跑赢行业\"评级\n\n北大荒 -5.365亿元 -29.39% 54.9% 机构预测2026全年归母净利润12.12亿，高分红属性，股息率约3.5%\n\n亚盛集团 2492万元 -2.629% 17.55% 甘肃农垦旗下，干旱地区种植标的\n\n2026-09-18 08:01:39 作者更新了以下内容\n\n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\n[股市如棋局，开户先布局，随时把握投资机遇！](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\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！**","东方财富财富号","2026-09-17T00:00:00Z",57,{"impact":18,"substance":19,"depth":76,"authority":77,"freshness":78,"relevant":21,"comment":79},12,4,9,"苏垦农发百万亩大田落地农业垂直智能体与低空遥感，属智慧农业规模化应用的产业级进展，但来源为财经自媒体、含大量荐股内容，信息增量与权威度有限，建议作为行业动态收录而非每日精选头条。",[81],{"name":72,"url":69},[26,27,28,83,84],"农业大模型","农垦",[86,87],"农业人工智能 农业大模型 低空经济 智慧农业","农业人工智能 农业大模型","农业人工智能农业大模型低空经济智慧农业-2849","2026-09-18T00:03:29.309992Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":8,"source_name":96,"source_url":93,"published_at":73,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"search_phrases":108,"slug":110,"view_count":36,"doi":111,"paper":112,"created_at":143},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics","论文",77,{"impact":18,"substance":52,"depth":100,"authority":101,"freshness":78,"relevant":21,"comment":102},17,13,"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[104],{"name":96,"url":93},[26,28,106,30,107],"精准农业","机器视觉",[109,63],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":111,"openalex_id":113,"authors":114,"venue":96,"cited_by_count":36,"oa_url":8,"card":136,"direction":140,"ingested_from":142},"W7213471057",[115,117,119,121,124,126,128,131,134],{"name":116,"orcid":8},"Shirun Gu",{"name":118,"orcid":8},"Xinyuan Fan",{"name":120,"orcid":8},"Lihui Zhu",{"name":122,"orcid":123},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":125,"orcid":8},"Lei Mu",{"name":127,"orcid":8},"Zichen Zhang",{"name":129,"orcid":130},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":132,"orcid":133},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":135,"orcid":8},"Zhiyuan Zhang",{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","农业人工智能与决策模型","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","openalex","2026-09-17T23:30:54.103781Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":156,"tags":158,"search_phrases":161,"slug":164,"view_count":36,"doi":165,"paper":166,"created_at":197},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment","2026-09-15T00:00:00Z",87,{"impact":17,"substance":154,"depth":18,"authority":53,"freshness":78,"relevant":21,"comment":155},23,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[157],{"name":150,"url":147},[26,28,159,30,160],"粮食安全","小麦收获",[162,163],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670","10.1016\u002Fj.rse.2026.115671",{"doi":165,"openalex_id":167,"authors":168,"venue":150,"cited_by_count":36,"oa_url":147,"card":191,"direction":195,"ingested_from":142},"W7213296259",[169,172,174,176,178,180,182,184,187,189],{"name":170,"orcid":171},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":173,"orcid":8},"Chongya Jiang",{"name":175,"orcid":8},"Jingwei An",{"name":177,"orcid":8},"Haokai Zhu",{"name":179,"orcid":8},"Yue Li",{"name":181,"orcid":8},"Xia Yao",{"name":183,"orcid":8},"Tao Cheng",{"name":185,"orcid":186},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":188,"orcid":8},"Weixing Cao",{"name":190,"orcid":8},"Yan Zhu",{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","农业遥感与作物表型","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":8,"source_name":204,"source_url":201,"published_at":151,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":49,"score_detail":205,"sources":208,"tags":210,"search_phrases":213,"slug":216,"view_count":36,"doi":217,"paper":218,"created_at":245},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",{"impact":18,"substance":17,"depth":206,"authority":19,"freshness":78,"relevant":21,"comment":207},19,"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。",[209],{"name":204,"url":201},[26,28,29,211,212],"水稻生产","多智能体控制",[214,215],"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":217,"openalex_id":219,"authors":220,"venue":204,"cited_by_count":36,"oa_url":201,"card":240,"direction":140,"ingested_from":142},"W7213225972",[221,224,226,228,231,233,235,237],{"name":222,"orcid":223},"Wenxiang Xu","https:\u002F\u002Forcid.org\u002F0000-0001-6476-4710",{"name":225,"orcid":8},"Xiaoyu Song",{"name":227,"orcid":8},"Liling Ye",{"name":229,"orcid":230},"Mengnan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5418-6347",{"name":232,"orcid":8},"He Zheng",{"name":234,"orcid":8},"Mingfeng Wang",{"name":236,"orcid":8},"Ze Liu",{"name":238,"orcid":239},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"tldr":241,"method":242,"finding":243,"direction":140,"opportunity":244},"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。","2026-09-16T23:30:01.996916Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":8,"content":8,"source_name":251,"source_url":8,"published_at":252,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":257,"tags":259,"search_phrases":262,"slug":265,"view_count":36,"doi":8,"paper":266,"created_at":275},2610,"整合人工智能、物联网与遥感技术的大田作物智能灌溉管理 综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260913082658847.htm","发表于Biosystems Engineering。对人工智能(AI)、物联网(IoT)和遥感(RS)技术在灌溉管理中的应用进行全面且结构化分析，特别是在优化基于天气、土壤和作物的灌溉调度方面。智能灌溉系统实现了水资源节约(用水量减少高达20-60%)、降低能源消耗和提高作物生产力。未来研究应优先考虑成本效益高的传感器开发和用户友好的AI界面。","Biosystems Engineering","2026-09-13T01:00:00Z",83,{"impact":17,"substance":255,"depth":18,"authority":19,"freshness":54,"relevant":21,"comment":256},21,"核心期刊综述，系统梳理AI、物联网与遥感在大田灌溉调度中的融合应用，给出节水20-60%等量化结论，对智慧农业技术路线有参考价值。",[258],{"name":251,"url":249},[26,28,260,261,30],"农业物联网","智能灌溉",[263,264],"农业人工智能 农业物联网 智慧农业 智能灌溉","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业智能灌溉-2610",{"doi":8,"openalex_id":8,"authors":267,"venue":8,"cited_by_count":36,"oa_url":8,"card":268,"direction":272,"ingested_from":274},[],{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"综述AI、物联网与遥感在大田作物智能灌溉调度中的应用与成效。","结构化综述AI、IoT、RS在基于天气、土壤和作物的灌溉调度中的应用。","智能灌溉可节水20-60%，降低能耗并提高作物生产力。","智慧农业 \u002F 农业物联网","低成本传感器与用户友好AI界面是落地瓶颈，可研究轻量化模型与低成本感知方案。","agent","2026-09-16T00:03:52.381950Z"]