[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3418":3,"related-3418":81},{"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":37,"classification":38,"search_phrases":75,"slug":78,"view_count":79,"doi":8,"paper":8,"created_at":80},3418,"国家重点研发计划项目'粮食生产大数据平台研发与应用'玉米智慧生产现场观摩会在北大荒红星农场召开","http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002Fc.html","9月19日国家重点研发计划项目'粮食生产大数据平台研发与应用'玉米智慧生产现场观摩会在北大荒集团红星农场举行。会议由中国农科院农业资源与农业区划研究所、北京市农林科学院信息技术研究中心共同主办。项目团队聚焦玉米生产全程大数据智能决策技术示范应用，展示智能分析算法在农机作业调度、无人机出苗率调查、变量精准施肥、苗情长势、病虫害监测和预警及估产等场景的应用。项目首席科学家杨贵军介绍无人机出苗率调查精度达98%以上，精准变量施肥效益达114元\u002F亩。",null,"[![Image 4](http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002Fc.html)](javascript:void(0))\n\n![Image 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“智”绘好“丰”景｜国家重点研发计划项目“粮食生产大数据平台研发与应用”玉米智慧生产现场观摩会在北大荒集团红星农场召开-新华网 \n\n![Image 11](http:\u002F\u002Fwww.news.cn\u002Fdetail2020\u002Fimages\u002Fewm.png)\n\n[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002Fc.html&title=%E2%80%9C%E6%99%BA%E2%80%9D%E7%BB%98%E5%A5%BD%E2%80%9C%E4%B8%B0%E2%80%9D%E6%99%AF%EF%BD%9C%E5%9B%BD%E5%AE%B6%E9%87%8D%E7%82%B9%E7%A0%94%E5%8F%91%E8%AE%A1%E5%88%92%E9%A1%B9%E7%9B%AE%E2%80%9C%E7%B2%AE%E9%A3%9F%E7%94%9F%E4%BA%A7%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%B9%B3%E5%8F%B0%E7%A0%94%E5%8F%91%E4%B8%8E%E5%BA%94%E7%94%A8%E2%80%9D%E7%8E%89%E7%B1%B3%E6%99%BA%E6%85%A7%E7%94%9F%E4%BA%A7%E7%8E%B0%E5%9C%BA%E8%A7%82%E6%91%A9%E4%BC%9A%E5%9C%A8%E5%8C%97%E5%A4%A7%E8%8D%92%E9%9B%86%E5%9B%A2%E7%BA%A2%E6%98%9F%E5%86%9C%E5%9C%BA%E5%8F%AC%E5%BC%80)\n\n![Image 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分享到：[](javascript:void(0))[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002Fc.html&title=%E2%80%9C%E6%99%BA%E2%80%9D%E7%BB%98%E5%A5%BD%E2%80%9C%E4%B8%B0%E2%80%9D%E6%99%AF%EF%BD%9C%E5%9B%BD%E5%AE%B6%E9%87%8D%E7%82%B9%E7%A0%94%E5%8F%91%E8%AE%A1%E5%88%92%E9%A1%B9%E7%9B%AE%E2%80%9C%E7%B2%AE%E9%A3%9F%E7%94%9F%E4%BA%A7%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%B9%B3%E5%8F%B0%E7%A0%94%E5%8F%91%E4%B8%8E%E5%BA%94%E7%94%A8%E2%80%9D%E7%8E%89%E7%B1%B3%E6%99%BA%E6%85%A7%E7%94%9F%E4%BA%A7%E7%8E%B0%E5%9C%BA%E8%A7%82%E6%91%A9%E4%BC%9A%E5%9C%A8%E5%8C%97%E5%A4%A7%E8%8D%92%E9%9B%86%E5%9B%A2%E7%BA%A2%E6%98%9F%E5%86%9C%E5%9C%BA%E5%8F%AC%E5%BC%80)[](javascript:void(0))[](javascript:void(0))\n\n![Image 15](http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002Fzxcode_202609204ab83ef006ef45ebb569a44384224294.jpg)\n\n![Image 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19](http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002F202609204ab83ef006ef45ebb569a44384224294_20260920289e26fed6564d5892aead2d3115c204.png)\n\n田间观摩现场科技感十足，技术人员并通过电脑终端远程操控无人机开展巡田作业，腾空而起的无人机对田间苗情、长势进行全方位、立体化勘查，无人机自动规划好田间作业方案。依托粮食生产大数据平台，田间玉米长势实现实时动态监测、精准智能分析，精准变量施肥设备按需下料、科学供肥，让玉米精准吸收养分、吃上“专属营养餐”。据项目首席科学家杨贵军介绍，无人机出苗率调查精度达到98%以上，精准变量施肥效益达到114元\u002F亩。通过大数据平台与田间生产管理深度融合，玉米生产全过程实现可视化、精准化、智能化管控，智能决策技术在玉米生产管理中的应用水平进一步提升。\n\n![Image 20](http:\u002F\u002Fhlj.xinhuanet.com\u002F20260920\u002F4ab83ef006ef45ebb569a44384224294\u002F202609204ab83ef006ef45ebb569a44384224294_2026092038fb455d8b09418288c656d18f8fb457.png)\n\n此次现场观摩会的成功举办，标志着“粮食生产大数据平台研发与应用”项目在玉米生产智能化领域取得重要阶段性进展。项目成果将为黑龙江地区数字化转型提供有力技术支撑，助力国家粮食安全保障能力持续提升。\n\n从种到收，大数据平台像给玉米生产装上了“智慧大脑”，让管理更精细、种植更省心。黑土地上，丰收的粮食与奔涌的数据流交相辉映，一幅科技赋能粮食安全的现代农业图景正徐徐展开。（通讯员：岳文 张洪溪）\n\n[【纠错】](javascript:void(0);)\n\n![Image 21](http:\u002F\u002Fwww.news.cn\u002Fimages\u002Fsyicon\u002Fspace.gif)\n\n 【责任编辑:董云竹】 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2026-09-20","2026-09-20T00:00:00Z","报道",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,16,14,13,5,1,"国家重点研发计划在北大荒红星农场的玉米智慧生产现场观摩，属国家级项目落地示范，有实质内容但偏活动通稿，时效稍弱。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准施肥","玉米","国家重点研发计划","粮食生产大数据",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,57,64,70],{"code":33,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[29],[29],[29],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"F3",4,[],[55],"施肥",[28],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"F5",3,[],[62],"农机",[],{"code":65,"score":59,"supported":46,"title":66,"abstract":67,"tags":69},"I3",[],[68],"估产",[],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"I1",[],[],[27],[76,77],"北大荒 红星农场 玉米 智慧生产","粮食生产大数据平台 玉米","北大荒红星农场玉米智慧生产-3418",6,"2026-09-25T00:09:31.337863Z",{"total":79,"page":22,"page_size":79,"items":82},[83,134,187,257,332,385],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":8,"content":88,"source_name":89,"source_url":8,"published_at":90,"category":12,"cover_url":8,"hotness":91,"is_selected":14,"score":92,"score_detail":93,"sources":96,"tags":101,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":128,"slug":131,"view_count":132,"doi":8,"paper":8,"created_at":133},3227,"国家重点研发计划项目\"粮食生产大数据平台研发与应用\"玉米智慧生产现场观摩会在北大荒集团红星农场召开","https:\u002F\u002Fhlj.chinadaily.com.cn\u002Fa\u002F202609\u002F19\u002FWS6aae78eae4b09a165c78b1e3.html","9-19 中国日报黑龙江报道：国家重点研发计划项目\"粮食生产大数据平台研发与应用\"玉米智慧生产现场观摩会在北大荒集团红星农场举行。会议由中国农业科学院农业资源与农业区划研究所、北京市农林科学院信息技术研究中心共同主办，北大荒集团北安分公司、红星农场协办。3000多亩玉米示范田连片成海，项目团队聚焦玉米生产全程大数据智能决策技术示范应用，集中展示了智能分析算法在农机作业调度、无人机出苗率调查、变量精准施肥、苗情长势、病虫害监测和预警及估产、种植规划和适宜收获期等场景的应用。据项目首席科学家杨贵军介绍，无人机出苗率调查精度达到98%以上，精准变量施肥效益达到114元\u002F亩。","![Image 4](https:\u002F\u002Fcn.chinadaily.com.cn\u002Fimage\u002F2025\u002Fsharelogo2.jpg)\n# 国家重点研发计划项目“粮食生产大数据平台研发与应用”玉米智慧生产现场观摩会在北大荒集团红星农场召开\n\n[![Image 5](https:\u002F\u002Fcn.chinadaily.com.cn\u002Fimage\u002F2025\u002Flogo_cntop.png)](https:\u002F\u002Fcn.chinadaily.com.cn\u002F)\n\n站内搜索\n\n*   站内搜索\n*   搜狗搜索\n*   中国搜索\n\n![Image 6](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fstatic\u002Fcommon\u002Fimg\u002Fj-q.jpg)\n\n[订阅](https:\u002F\u002Fwww.chinadaily.com.cn\u002Fe\u002Fsubscribe)[移动](https:\u002F\u002Fwww.chinadaily.com.cn\u002Fe\u002Fstatic_e\u002Fnewmedia)[English](https:\u002F\u002Fwww.chinadaily.com.cn\u002F)\n\n*   [首页](https:\u002F\u002Fcn.chinadaily.com.cn\u002F)\n*   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16](https:\u002F\u002Fwww.chinadaily.com.cn\u002Fimage_e\u002F2020\u002Ftimg.jpg)\n\n秋色初染，沃野铺金。9月19日，国家重点研发计划项目“粮食生产大数据平台研发与应用”玉米智慧生产现场观摩会在北大荒集团红星农场举行。\n\n会议由中国农业科学院农业资源与农业区划研究所、北京市农林科学院信息技术研究中心共同主办，北大荒集团北安分公司、红星农场协办。来自农业农村部种植业管理司、农业农村部大数据发展中心、黑龙江省农垦科学院、中国科学院东北地理与农业生态研究所、东北农业大学、黑龙江省农业科学院、北大荒集团北安分公司、红星农场等单位的领导和专家到会指导，连同项目参加单位代表共70余人参会。\n\n![Image 17](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e62f.jpeg)\n\n此时，在红星农场示范区，3000多亩玉米示范田连片成海，挺拔的玉米秆上，饱满的玉米棒透着金黄，秋风拂过，沙沙作响，一派喜人的丰收景象。项目团队聚焦玉米生产全程大数据智能决策技术示范应用，围绕玉米产前、产中、产后各关键环节，集中展示了智能分析算法在农机作业调度、无人机出苗率调查、变量精准施肥、苗情长势、病虫害监测和预警及估产、种植规划和适宜收获期等场景的应用。\n\n![Image 18](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e631.jpeg)\n\n田间观摩现场科技感十足，技术人员并通过电脑终端远程操控无人机开展巡田作业，腾空而起的无人机对田间苗情、长势进行全方位、立体化勘查，无人机自动规划好田间作业方案。依托粮食生产大数据平台，田间玉米长势实现实时动态监测、精准智能分析，精准变量施肥设备按需下料、科学供肥，让玉米精准吸收养分、吃上“专属营养餐”。\n\n据项目首席科学家杨贵军介绍，无人机出苗率调查精度达到98%以上，精准变量施肥效益达到114元\u002F亩。通过大数据平台与田间生产管理深度融合，玉米生产全过程实现可视化、精准化、智能化管控，智能决策技术在玉米生产管理中的应用水平进一步提升。\n\n![Image 19](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e633.jpeg)\n\n此次现场观摩会的成功举办，标志着“粮食生产大数据平台研发与应用”项目在玉米生产智能化领域取得重要阶段性进展。项目成果将为黑龙江地区数字化转型提供有力技术支撑，助力国家粮食安全保障能力持续提升。\n\n从种到收，大数据平台像给玉米生产装上了“智慧大脑”，让管理更精细、种植更省心。黑土地上，丰收的粮食与奔涌的数据流交相辉映，一幅科技赋能粮食安全的现代农业图景正徐徐展开。（中国日报黑龙江记者站 编辑：周慧颖 通讯员：岳文 张洪溪）\n\n 【责任编辑：舒靓】 \n\nCOMPO\n\nWS6aae78eae4b09a165c78b1e3\n\nhttps:\u002F\u002Fhlj.chinadaily.com.cn\u002Fa\u002F202609\u002F19\u002FWS6aae78eae4b09a165c78b1e3.html\n\n[![Image 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\n\n×","中国日报网","2026-09-19T00:00:00Z",25,71,{"impact":17,"substance":18,"depth":20,"authority":20,"freshness":94,"relevant":22,"comment":95},7,"国家重点研发计划项目在北大荒红星农场的玉米智慧生产现场观摩，属全国性科研项目落地示范，有实质内容但偏会议通稿，时效稍滞后。",[97,98],{"name":89,"url":86},{"name":99,"url":100},"中国农业科学院 2026年09月19日","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002Fe4643988980f4485951c01680b70039b.htm",[27,29,30,102,31],"北大荒",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":106},{"candidates":107,"agricultural_evidence":46},[108,112,116,120,124],{"code":33,"score":45,"supported":46,"title":109,"abstract":110,"tags":111},[29],[29],[29],{"code":51,"score":59,"supported":46,"title":113,"abstract":114,"tags":115},[],[55],[],{"code":58,"score":59,"supported":46,"title":117,"abstract":118,"tags":119},[],[62],[],{"code":65,"score":59,"supported":46,"title":121,"abstract":122,"tags":123},[],[68],[],{"code":71,"score":22,"supported":14,"title":125,"abstract":126,"tags":127},[],[],[27],[129,130],"北大荒 红星农场 玉米智慧生产","粮食生产大数据平台 观摩会","北大荒红星农场玉米智慧生产-3227",2,"2026-09-23T00:04:30.284365Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":8,"content":139,"source_name":140,"source_url":8,"published_at":141,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":147,"tags":149,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":181,"slug":184,"view_count":185,"doi":8,"paper":8,"created_at":186},5359,"千金方变量处方图技术在北大荒鹤山农场测产:玉米增产 6.9% 尿素减 5.7%","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696358451626181183\u002F","依托智慧农场技术与系统全国重点实验室平台,由东北农业大学与北大荒集团鹤山农场有限公司联合主办的智慧农作千金方变量处方图技术测产评估会在鹤山农场举办。千金方变量处方图技术在玉米、大豆两种作物上均实现中低产田的增产节本:玉米变量处方区较定量作业区增产 6.9%、尿素投入降低 5.7%,每公顷收益增加约 470 元;大豆变量处方区较对照区增产 2.3%,每公顷收益最高增加约 350 元。技术由东北农业大学智慧农业团队研发。","## 田间开出“千金方” 黑土算准“增收账”\n\n日前，依托智慧农场技术与系统全国重点实验室平台，由东北农业大学与北大荒集团鹤山农场有限公司联合主办的智慧农作“千金方”变量处方图技术测产评估会在鹤山农场举办。经第三方专家取点测产和实收测产，“千金方”变量处方图技术在玉米、大豆两种作物上均实现“中低产田”的增产节本：玉米变量处方区较定量作业区增产6.9%、尿素投入降低5.7%，每公顷收益增加约470元；大豆变量处方区较对照区增产2.3%，每公顷收益最高增加约350元。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F29c175dfbe394a34af43ada958310b56~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069548&x-signature=wosebaJveF3ksDGq7EEr4PTRctk%3D)\n活动现场\n\n测产环节，邀请嫩江市农业技术推广中心刘月辉研究员担任测产组组长，联合各专家团队，严格按照测产标准科学选点取样，通过人工点测、机械实收相结合的方式进行测定，检验玉米精准变量处方技术的应用成效。专家团现场报告实测结果：点测结果显示，变量处方图技术区较定量作业区平均增产5.7%；机械实收结果显示，变量处方图技术区产量达12.4吨\u002F公顷，定量区为11.6吨\u002F公顷，增产6.9%，同时玉米尿素投入降低5.7%，每公顷收益增加约470元。\n\n此前9月26日，东北农业大学智慧农业团队组织黑龙江省农垦科学院、嫩江农业技术推广中心和鹤山农场专家对“大豆种植全流程‘三变’处方技术”执行地块进行田间实收测产。该田块为中低产田，其中变量作业区10.18公顷，对照区14.23公顷，测产结果显示变量技术作业区平均公顷产量较对照区增产2.3%，机收产量3.11吨\u002F公顷，每公顷收益增加约350元。\n\n东北农业大学副校长陈庆山表示，东北农业大学与九三分公司长期保持协同联动合作，鹤山农场为科研团队提供了良好的田间试验用地和高中低产地块场景，为各项科研技术攻关、成果验证提供了有力支持。双方将扎根生产一线破解实际难题，以大数据、智能技术实现精准变量作业，最大限度挖掘土地增产潜力。学校将持续发挥科研、人才、技术优势，深化产学研融合，推动智慧农业技术落地生根、迭代完善，助力农业向信息化、数字化、智能化更深层次发展。\n\n据介绍，“千金方”技术由东北农业大学智慧农业团队研发，以气象和农田信息数据为基础，以作物信息技术和遥感技术为核心，融合GIS技术对地块间和地块内实现差异化对齐，通过“三看”到“三变”的处方图技术完成耕地水肥资源分配不均衡下的信息诊断和精准化、智能化的农事作业管理。历经3年研发和1年示范，在鹤山农场实现种肥精准调控，在优化肥料投入前提下实现大豆、玉米单产提升，验证了变量处方播种、变量追肥一体化技术在生产田上的增产增效效果，为黑土丘陵区精准变量作业趟出了一条可复制的路子。\n\n下一步，各方将持续深化产学研用协同合作，立足北大荒黑土地资源禀赋，不断优化完善精准变量处方图技术体系，补齐技术落地推广中的现实问题，把科研方案转化为大田可复制、易推广的实操模式。面向现代农业发展需求，进一步凝聚智慧农场技术与系统全国重点实验室科技力量，打造北方寒地智慧农场建设模式，加快智慧农业技术成果转化应用，持续提升粮食综合生产能力，为北大荒集团现代农业高质量发展、筑牢国家粮食安全“压舱石”贡献科技力量。\n\n（图片由东北农业大学提供）\n\n记者 赵一诺","黑龙江新闻网(今日头条) 2026-10-14","2026-10-14T00:00:00Z",68,{"impact":144,"substance":17,"depth":18,"authority":145,"freshness":59,"relevant":22,"comment":146},18,9,"第三方实收测产数据扎实，变量处方技术增产节本效果明确，具可复制推广价值，但时效偏旧、属区域示范进展。",[148],{"name":140,"url":137},[27,28,150,151,152],"黑土地保护","玉米大豆","变量处方图",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":156},{"candidates":157,"agricultural_evidence":46},[158,162,166,172,176],{"code":33,"score":45,"supported":46,"title":159,"abstract":160,"tags":161},[29],[29],[151],{"code":71,"score":52,"supported":46,"title":163,"abstract":164,"tags":165},[],[27],[27],{"code":167,"score":59,"supported":46,"title":168,"abstract":169,"tags":171},"A4",[],[170],"大豆",[],{"code":51,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[28],{"code":177,"score":22,"supported":14,"title":178,"abstract":179,"tags":180},"H1",[],[],[150],[182,183],"东北农业大学 千金方 变量处方图","鹤山农场 玉米 测产","东北农业大学千金方变量处方图-5359",0,"2026-10-20T02:47:45.290758Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":194,"category":195,"cover_url":8,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":207,"classification":214,"search_phrases":235,"slug":238,"view_count":185,"doi":239,"paper":240,"created_at":256},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z","论文",63,{"impact":198,"substance":144,"depth":18,"authority":45,"freshness":145,"relevant":22,"comment":199},8,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[201],{"name":193,"url":190},[27,203,204,29,205],"农业人工智能","机器学习","耐盐育种",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[208],{"code":209,"slug":210,"name":211,"path":212,"parent_code":213},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":215},{"candidates":216,"agricultural_evidence":46},[217,224,229],{"code":209,"score":218,"supported":46,"title":219,"abstract":221,"tags":223},19,[220],"machine learning",[204,220,222],"decision support",[203,204],{"code":33,"score":225,"supported":46,"title":226,"abstract":227,"tags":228},15,[34],[29,34],[29],{"code":71,"score":94,"supported":46,"title":230,"abstract":231,"tags":234},[],[232,233],"精准农业","precision agriculture",[27],[236,237],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":239,"openalex_id":241,"authors":242,"venue":193,"cited_by_count":185,"oa_url":190,"card":249,"direction":253,"ingested_from":255},"W7221462188",[243,246],{"name":244,"orcid":245},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":247,"orcid":248},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","农业人工智能与决策模型","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","openalex","2026-10-12T23:30:44.762497Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":8,"source_name":263,"source_url":260,"published_at":264,"category":195,"cover_url":8,"hotness":13,"is_selected":14,"score":92,"score_detail":265,"sources":269,"tags":271,"primary_topic":275,"secondary_topics":276,"classification":282,"search_phrases":314,"slug":317,"view_count":22,"doi":318,"paper":319,"created_at":331},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",{"impact":45,"substance":266,"depth":267,"authority":20,"freshness":145,"relevant":22,"comment":268},20,17,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[270],{"name":263,"url":260},[27,272,29,232,273,274],"变量施肥","遥感监测","养分管理",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[277],{"code":51,"slug":278,"name":279,"path":280,"parent_code":281},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":283},{"candidates":284,"agricultural_evidence":46},[285,289,293,299,306,310],{"code":71,"score":18,"supported":46,"title":286,"abstract":287,"tags":288},[233],[232,233],[27,232],{"code":33,"score":225,"supported":46,"title":290,"abstract":291,"tags":292},[34],[29,34],[29],{"code":51,"score":294,"supported":46,"title":295,"abstract":296,"tags":298},11,[],[55,297,274],"肥料",[274,272],{"code":300,"score":79,"supported":46,"title":301,"abstract":302,"tags":305},"H2",[],[303,304],"土壤","soil",[],{"code":209,"score":59,"supported":46,"title":307,"abstract":308,"tags":309},[],[204],[],{"code":65,"score":22,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[273],[315,316],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":318,"openalex_id":320,"authors":321,"venue":263,"cited_by_count":185,"oa_url":260,"card":326,"direction":253,"ingested_from":255},"W7221366291",[322,324],{"name":323,"orcid":8},"Piyali Pal",{"name":325,"orcid":8},"Disha Chattopadhyay",{"tldr":327,"method":328,"finding":329,"direction":253,"opportunity":330},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":8,"content":8,"source_name":337,"source_url":8,"published_at":8,"category":195,"cover_url":8,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":371,"slug":374,"view_count":132,"doi":8,"paper":375,"created_at":384},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":144,"substance":17,"depth":144,"authority":20,"freshness":59,"relevant":22,"comment":340},"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[342],{"name":337,"url":335},[27,344,204,29,232,345],"产量预测","遥感",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":349},{"candidates":350,"agricultural_evidence":46},[351,355,359,363,367],{"code":71,"score":20,"supported":46,"title":352,"abstract":353,"tags":354},[232],[232],[27,232],{"code":33,"score":45,"supported":46,"title":356,"abstract":357,"tags":358},[29],[29],[29],{"code":65,"score":21,"supported":46,"title":360,"abstract":361,"tags":362},[],[345],[344,345],{"code":209,"score":52,"supported":46,"title":364,"abstract":365,"tags":366},[],[204],[204],{"code":300,"score":59,"supported":46,"title":368,"abstract":369,"tags":370},[],[303],[],[372,373],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",{"doi":8,"openalex_id":8,"authors":376,"venue":8,"cited_by_count":185,"oa_url":8,"card":377,"direction":381,"ingested_from":383},[],{"tldr":378,"method":379,"finding":380,"direction":381,"opportunity":382},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","智慧农业 \u002F 农业物联网","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":8,"source_name":391,"source_url":388,"published_at":392,"category":195,"cover_url":8,"hotness":91,"is_selected":14,"score":393,"score_detail":394,"sources":396,"tags":401,"primary_topic":405,"secondary_topics":406,"classification":413,"search_phrases":443,"slug":446,"view_count":185,"doi":447,"paper":448,"created_at":466},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z",80,{"impact":144,"substance":17,"depth":144,"authority":20,"freshness":145,"relevant":22,"comment":395},"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[397,398],{"name":391,"url":388},{"name":399,"url":400},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[402,27,29,403,404],"数字乡村","农业碳排放","区块链",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[407],{"code":408,"slug":409,"name":410,"path":411,"parent_code":412},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":414},{"candidates":415,"agricultural_evidence":46},[416,420,426,432,438],{"code":33,"score":225,"supported":46,"title":417,"abstract":418,"tags":419},[34],[29,34],[29],{"code":408,"score":45,"supported":46,"title":421,"abstract":423,"tags":425},[422],"carbon emission",[424],"碳排放",[403],{"code":71,"score":94,"supported":46,"title":427,"abstract":428,"tags":431},[],[429,430],"数字农业","digital agriculture",[27],{"code":433,"score":59,"supported":46,"title":434,"abstract":435,"tags":437},"G2",[],[436],"社会化服务",[],{"code":439,"score":22,"supported":14,"title":440,"abstract":441,"tags":442},"I6",[],[],[402],[444,445],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416","10.3389\u002Ffsufs.2026.1942225",{"doi":447,"openalex_id":449,"authors":450,"venue":391,"cited_by_count":185,"oa_url":459,"card":460,"direction":381,"ingested_from":255},"W7220869193",[451,454,456],{"name":452,"orcid":453},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":455,"orcid":8},"Zhiyi Hu",{"name":457,"orcid":458},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":461,"method":462,"finding":463,"direction":464,"opportunity":465},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","农业绿色发展与碳","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","2026-10-08T23:30:07.668939Z"]