[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2556":3,"related-2556":75},{"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,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":69,"slug":72,"view_count":73,"doi":8,"paper":8,"created_at":74},2556,"行业首个种植业可信数据空间在宁波发布","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7684162574829404672","9月11日第十六届智慧城市与智能经济博览会期间发布。浙江省农业农村厅、浙江省数据局、拱墅区政府、中国联通浙江省分公司、中国农科院茶叶研究所、中国水稻研究所、中国信通院等参与。锚定10类场景、30个数据产品、5个高质量数据集；建立以规范治理、协同联动、合理激励为核心的运营机制；推动农业数据与人工智能协同应用。",null,"## 种地也要拼数据了 行业首个种植业可信数据空间在宁波发布\n\n2026-09-11 14:35·[浙江日报](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAATrbOByn_34fAjCz7FVgDhykDJtg-bDdUW1ZdzpQq3xc\u002F?source=tuwen_detail)\n\n潮新闻 记者 王波 通讯员 张梦瑶\n\n9月11日上午，第十六届智慧城市与智能经济博览会期间，农业（种植）行业可信数据空间阶段性成果在宁波正式发布。\n\n来自浙江省农业农村厅、省数据局、拱墅区政府、中国联通浙江省分公司、中国农业科学院茶叶研究所、中国水稻研究所、中国信通院等支撑单位以及省内外农业、保险、云计算等行业领域的用户企业和相关合作单位代表参加了发布仪式。浙江省农业农村厅党组成员、省畜牧农机发展中心党委书记、主任骆健民，浙江省数据局党组成员、副局长吴旭升，杭州市拱墅区人民政府党组成员、副区长张扬，中国联通浙江省分公司党委委员、副总经理戴苓，中国人保浙江省分公司党委委员、副总经理傅宏刚等出席活动。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F19e9822ce53c469586ad3e0eab41c665~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121964&x-signature=CZMmG1XKeG9OqROi9p2%2FmIMYqMk%3D)\n发布现场。通讯员 供图\n\n农业（种植）行业可信数据空间是在浙江省农业农村厅的大力支持和指导下，由杭州拱墅区委区政府联合各生态单位，全力推进农业（种植）行业可信数据空间试点申报。项目锚定“10类场景、30个数据产品、5个高质量数据集”，持续探索农业数据资源的汇聚、治理、流通和应用。\n\n从农业数据资源汇聚，到可信流通机制探索，再到数据产品和应用场景落地，农业（种植）行业可信数据空间正在逐步形成更加完善的建设体系。\n\n在数智赋能方面，联通数据智能有限公司政务行业部总经理康晓宇围绕“建设种植全链可信数智底座 赋能现代农业高质量发展”进行了专题介绍，系统阐释联通助力农业（种植）行业可信数据空间的数智底座、创新探索、技术沉淀。目前空间建立了以规范治理、协同联动、合理激励为核心的运营机制。构建协同治理与激励分配体系，吸引多元主体参与生态。\n\n在数据安全与质量保障方面，托普云农智慧农业研究院执行副院长姚晟围绕“数据空间高质量数据集及场景应用”进行了具体介绍，依托“乡村大脑”、省域植保库及“问稷”农业大模型等能力，空间将进一步推动农业数据与人工智能协同应用，为农业生产决策、农技服务、灾害预警、植保管理等场景提供数据支撑。\n\n在确保可信安全方面，空间围绕农业数据全生命周期管理，探索建立覆盖数据归集、治理、授权、开发、流通、应用等环节的安全可信机制，通过数据脱敏、权限控制、可信流通等技术手段，保障农业数据在合规前提下实现安全使用和价值释放。同时，围绕数据来源可信、使用过程可控、流通过程可溯等要求，持续完善数据空间相关技术规则和运营机制，推动参与主体可信任、数据资源可确认、数据使用可保障、流通过程可追溯，为农业数据要素市场化配置提供安全底座。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0fc054aa19faedaef6e05e4fdad70376~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121964&x-signature=xU4HBevJU%2BiYkyJxYVDTSx0N4Os%3D)\n活动现场。通讯员 供图\n\n空间面向农业（种植）领域持续构建开放协同的数据生态，目前已形成由农业主管部门、科研院所、产业企业、数据服务机构等多方参与的生态体系。活动现场，举行了首批入驻农业（种植）行业可信数据空间用户代表授牌仪式，中国农业科学院茶叶研究所、中国水稻研究所、中国联通农业军团、联通乡村振兴研究院、浙江省农业科学院、浙江省兴合集团有限责任公司、阿里云计算有限公司、浙江托普云农科技股份有限公司、浙江天演维真网络科技股份有限公司、浙江宇联信息发展有限公司、金华浙农信息技术股份有限公司等11家单位成为首批入驻用户。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fe26240b8cdec329c5fe07833d7279c20~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121964&x-signature=wF5GX7HQSSzSeqzyYVA2%2Frox6mI%3D)\n首批入驻企业。通讯员 供图\n\n下一步，空间将持续链接更多农业数据供需、技术、服务等主体，共同探索农业数据要素创新发展机遇，推动农机行业数字化转型与农业效率提升，为乡村振兴、粮食安全等国家战略落地提供了可信、可持续的数据赋能路径。\n\n“转载请注明出处”","浙江日报 2026-09-11","2026-09-11T07:00:00Z","报道",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},22,18,15,6,1,"行业首个种植业可信数据空间落地，属细分领域标志性进展，多方信源与具体建设目标支撑，但发布已过一周、偏阶段性成果通报，适合精选但非头条级。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业数据要素","可信数据空间","种植业数字化",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":68},[38,46,52,58,63],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"A1",3,true,[],[44],"水稻",[],{"code":47,"score":40,"supported":41,"title":48,"abstract":49,"tags":51},"B3",[],[50],"茶叶",[],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I6",2,[],[],[28,29],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[26],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I2",[],[],[27],"多个产业证据相当，需要确认主归属",[70,71],"农业人工智能 农业数据要素 可信数据空间 种植业数字化","农业人工智能 农业数据要素","农业人工智能农业数据要素可信数据空间种植业数字化-2556",0,"2026-09-16T00:03:45.327038Z",{"total":20,"page":21,"page_size":20,"items":76},[77,130,234,311,379,444],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":8,"content":82,"source_name":83,"source_url":8,"published_at":84,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":92,"tags":94,"primary_topic":8,"secondary_topics":96,"classification":97,"search_phrases":126,"slug":128,"view_count":54,"doi":8,"paper":8,"created_at":129},2698,"农业(种植)行业可信数据空间阶段性成果发布——11家单位首批入驻","https:\u002F\u002Fzj.sina.cn\u002Fningbo\u002Fnews\u002F2026-09-13\u002Fdetail-inirpqmh8601035.d.html?pos=339","农业(种植)行业可信数据空间阶段性成果发布,空间建立以规范治理、协同联动、合理激励为核心的运营机制,11家单位首批入驻:中国农科院茶叶所、中国水稻研究所、中国联通农业军团、联通乡村振兴研究院、浙江省农科院、浙江省兴合集团、阿里云、托普云农、天演维真、宇联信息、金华浙农信息技术。","[](https:\u002F\u002Fsina.cn\u002F?vt=4 \"手机新浪网\")\n## [新浪浙江](http:\u002F\u002Fzj.sina.cn\u002F?vt=4 \"新浪浙江\")[资讯](http:\u002F\u002Fzj.sina.cn\u002Fningbo\u002Fnews\u002Flist-p1.d.html?vt=4)\n\n[](http:\u002F\u002Fnews.sina.cn\u002Fguide.d.html?vt=4 \"网站导航\")[![Image 1](blob:http:\u002F\u002Flocalhost\u002Fa60b3ebabb24ca182698fd8650939101)](https:\u002F\u002Fmy.sina.cn\u002F?vt=4)\n## 农业（种植）行业可信数据空间阶段性成果发布\n\n_![Image 2](https:\u002F\u002Fn.sinaimg.cn\u002Fdefault\u002Ftransform\u002F360\u002Fw180h180\u002F20230214\u002F38ee-388f40e0914913fbe0ec2a6aab6a7ce5.jpg)_\n\n## 商讯\n\n09月12日 10:56 关注 \n\n_**摘要：**此次发布的农业（种植）行业可信数据空间是在浙江省农业农村厅的大力支持和指导下，由杭州拱墅区委区政府联合各生态单位，全力推进农业（种植）行业可信数据空间试点申报。项目锚定“10类场景、30个数据产品、5个高质量数据集”，持续探索农业数据资源的汇聚、治理、流通和应用。_![Image 3](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fzj\u002Ftransform\u002F59\u002Fw550h309\u002F20260912\u002Fcae1-08dce1bef2eec5a5514c1e7851896e92.png\u002Fw700h350z1l10t10e26.jpg)\n9月11日上午，第十六届智慧城市与智能经济博览会期间，农业（种植）行业可信数据空间阶段性成果在宁波正式发布。来自浙江省农业农村厅、省数据局、拱墅区政府、中国联通浙江省分公司、中国农业科学院茶叶研究所、中国水稻研究所、中国信通院等支撑单位以及省内外农业、保险、云计算等行业领域的用户企业和相关合作单位代表参加了发布仪式。浙江省农业农村厅党组成员、省畜牧农机发展中心党委书记、主任骆健民，浙江省数据局党组成员、副局长吴旭升，杭州市拱墅区人民政府党组成员、副区长张扬，中国联通浙江省分公司党委委员、副总经理戴苓，中国人保浙江省分公司党委委员、副总经理傅宏刚等出席活动。\n\n[![Image 4](https:\u002F\u002Fn.sinaimg.cn\u002Fzj\u002Ftransform\u002F59\u002Fw550h309\u002F20260912\u002Fcae1-08dce1bef2eec5a5514c1e7851896e92.png)](javascript:void(0))\n此次发布的农业（种植）行业可信数据空间是在浙江省农业农村厅的大力支持和指导下，由杭州拱墅区委区政府联合各生态单位，全力推进农业（种植）行业可信数据空间试点申报。项目锚定“10类场景、30个数据产品、5个高质量数据集”，持续探索农业数据资源的汇聚、治理、流通和应用。\n\n从农业数据资源汇聚，到可信流通机制探索，再到数据产品和应用场景落地，农业（种植）行业可信数据空间正在逐步形成更加完善的建设体系。\n\n在数智赋能方面，联通数据智能有限公司政务行业部总经理康晓宇围绕“建设种植全链可信数智底座 赋能现代农业高质量发展”进行了专题介绍，系统阐释联通助力农业（种植）行业可信数据空间的数智底座、创新探索、技术沉淀。目前空间建立了以规范治理、协同联动、合理激励为核心的运营机制。构建协同治理与激励分配体系，吸引多元主体参与生态。\n\n在数据安全与质量保障方面，托普云农智慧农业研究院执行副院长姚晟围绕“数据空间高质量数据集及场景应用”进行了具体介绍，依托“乡村大脑”、省域植保库及“问稷”农业大模型等能力，空间将进一步推动农业数据与人工智能协同应用，为农业生产决策、农技服务、灾害预警、植保管理等场景提供数据支撑。\n\n在确保可信安全方面，空间围绕农业数据全生命周期管理，探索建立覆盖数据归集、治理、授权、开发、流通、应用等环节的安全可信机制，通过数据脱敏、权限控制、可信流通等技术手段，保障农业数据在合规前提下实现安全使用和价值释放。同时，围绕数据来源可信、使用过程可控、流通过程可溯等要求，持续完善数据空间相关技术规则和运营机制，推动参与主体可信任、数据资源可确认、数据使用可保障、流通过程可追溯，为农业数据要素市场化配置提供安全底座。\n\n[![Image 5](https:\u002F\u002Fn.sinaimg.cn\u002Fzj\u002Ftransform\u002F59\u002Fw550h309\u002F20260912\u002F322a-c028a228cddd5828d276dba5555bad32.png)](javascript:void(0))\n空间面向农业（种植）领域持续构建开放协同的数据生态，目前已形成由农业主管部门、科研院所、产业企业、数据服务机构等多方参与的生态体系。活动现场，举行了首批入驻农业（种植）行业可信数据空间用户代表授牌仪式，中国农业科学院茶叶研究所、中国水稻研究所、中国联通农业军团、联通乡村振兴研究院、浙江省农业科学院、浙江省兴合集团有限责任公司、阿里云计算有限公司、浙江托普云农科技股份有限公司、浙江天演维真网络科技股份有限公司、浙江宇联信息发展有限公司、金华浙农信息技术股份有限公司等11家单位成为首批入驻用户。\n\n[![Image 6](https:\u002F\u002Fn.sinaimg.cn\u002Fzj\u002Ftransform\u002F59\u002Fw550h309\u002F20260912\u002Fbd30-051313479530cf5d898cb508039da8ba.png)](javascript:void(0))\n下一步，空间将持续链接更多农业数据供需、技术、服务等主体，共同探索农业数据要素创新发展机遇，推动农机行业数字化转型与农业效率提升，为乡村振兴、粮食安全等国家战略落地提供了可信、可持续的数据赋能路径。\n\n![Image 7](https:\u002F\u002Fn.sinaimg.cn\u002Fwww\u002Findex\u002Fsina_logo.png)\n\n## 相关新闻\n\n## 推荐阅读\n\n加载中...","新浪浙江 2026年9月13日","2026-09-13T00:00:00Z",67,{"impact":87,"substance":18,"depth":88,"authority":89,"freshness":90,"relevant":21,"comment":91},20,14,7,8,"浙江省级农业种植可信数据空间发布阶段性成果并公布首批11家入驻单位，属细分领域实质进展，但为地方媒体商讯通稿，权威性与全国影响力有限。",[93],{"name":83,"url":80},[95,26,27,28,29],"数字乡村",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":98},{"candidates":99,"agricultural_evidence":41,"reason":68},[100,104,108,114,118,122],{"code":39,"score":40,"supported":41,"title":101,"abstract":102,"tags":103},[],[44],[],{"code":47,"score":40,"supported":41,"title":105,"abstract":106,"tags":107},[],[50],[],{"code":109,"score":40,"supported":41,"title":110,"abstract":111,"tags":113},"G",[],[112],"乡村振兴",[],{"code":53,"score":40,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[28,29,95],{"code":59,"score":21,"supported":14,"title":119,"abstract":120,"tags":121},[],[],[26],{"code":64,"score":21,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[27],[127,71],"农业人工智能 农业数据要素 可信数据空间 数字乡村","农业人工智能农业数据要素可信数据空间数字乡村-2698","2026-09-17T00:04:37.033876Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":8,"content":135,"source_name":136,"source_url":8,"published_at":137,"category":138,"cover_url":8,"hotness":13,"is_selected":41,"score":139,"score_detail":140,"sources":146,"tags":148,"primary_topic":152,"secondary_topics":158,"classification":159,"search_phrases":229,"slug":232,"view_count":89,"doi":8,"paper":8,"created_at":233},3924,"山东省人民政府办公厅印发《关于大力发展智慧农业的实施意见》（鲁政办字〔2026〕85号）","https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html","《意见》提出到2030年山东将建设80个数字农业发展县，重点打造300个高水平智慧农业应用场景，全省农业生产信息化率达到60%以上，粮油作物水肥一体化应用面积达到2000万亩。围绕\"加力推进优势产业智慧转型\"，《意见》部署智慧赋能主要作物单产提升、设施农业改造升级、畜牧业节本增效、渔业产业提档升级四大方向，加快\"北斗+智慧农业\"应用推广，构建小麦、玉米、大豆、花生等主要作物数字化种植技术体系；建设省级种质资源数据库平台，推动经验育种向智能设计育种转变。","*   [](https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html)\n\n*   [](https:\u002F\u002Fny.zibo.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_5879_3024122.html)\n\n[首页](https:\u002F\u002Fny.zibo.gov.cn\u002Findex.html)>[三农业务](https:\u002F\u002Fny.zibo.gov.cn\u002Fcol\u002Fcol980\u002Findex.html)>[政策法规](https:\u002F\u002Fny.zibo.gov.cn\u002Fcol\u002Fcol5879\u002Findex.html)\n\n山东省人民政府办公厅关于大力发展智慧农业的实施意见\n* * *\n发布日期： 2026-09-30 13:58:48 浏览次数:字体：[ [大](javascript:doZoom(17))[中](javascript:doZoom(15))[小](javascript:doZoom(13)) ]\n鲁政办字〔2026〕85号\n\n各市人民政府，各县（市、区）人民政府，省政府有关部门、有关直属机构，有关高等院校：\n\n为贯彻落实党中央、国务院关于智慧农业的决策部署，深入实施“人工智能＋农业”行动，加快推进物联网、大数据、人工智能、机器人等现代信息技术在农业农村领域全方位、全链条推广应用，打造全国智慧农业发展高地，助力农业农村现代化，现提出如下实施意见。\n\n**一、加力推进优势产业智慧转型**\n\n（一）智慧赋能主要作物单产提升。利用卫星遥感、无人机、地面传感等技术，统筹布局“四情”监测网络，深化农业气象灾害风险减量工作，提升实时感知和预警能力。推进农业生产数字化改造，大力推广应用智能农业机械化、水肥一体化智慧管控等技术，加强农田新型基础设施建设和改造升级，探索粮食减损智能系统推广，到2030年粮油作物水肥一体化应用面积达到2000万亩。促进良田、良种、良机、良法与数字化技术有机融合，开展“北斗＋智慧农业”支撑粮油作物大面积单产提升应用推广，构建小麦、玉米、大豆、花生等主要作物数字化种植技术体系，提升大面积单产水平。（牵头单位：省农业农村厅，责任单位：省大数据局、省气象局）\n\n（二）智慧赋能设施农业改造升级。有序推进集中连片老旧低效设施蔬菜基地、果园、茶园等更新改造，建设标准化、智慧化现代设施。加快精准感知、智能调控等数字化技术装备集成应用，推广应用设施农业智能体，实现全生长周期智能化决策。引导规模化设施农业经营主体，部署应用生产经营全过程数字化管理平台，推进设施农业智慧化管理。（牵头单位：省农业农村厅，责任单位：省发展改革委、省自然资源厅、省大数据局）\n\n（三）智慧赋能畜牧业节本增效。引导规模养殖场（户）进行数字化改造，建立电子养殖档案，推进养殖、饲料兽药、屠宰等环节数据互联互通，实现直联直报。鼓励规模养殖场（户）、畜牧屠宰企业加强重点环节监测预警，完善从生产到屠宰的全链条数字化监管。大力推广精准饲喂、智能环控、自动巡检、个体识别等智慧化养殖装备与技术，全面提高综合养殖效益。（牵头单位：省畜牧局，责任单位：省农业农村厅、省大数据局）\n\n（四）智慧赋能渔业产业提档升级。分类有序推进池塘、工厂化等养殖模式智能化改造，科学配备饲料精准投喂、智能增氧、循环水处理、水质在线监测等智能化设施。深化海洋牧场现代化建设，因地制宜推广应用水域环境监测、水下巡检、智能采捕等先进技术装备。推进苗种繁育、健康养殖、产品加工、冷链物流、综合服务等全链条数字化融合发展。强化渔政执法数字化建设，加快海洋渔船设施装备更新改造，利用渔船监控平台，推广AIS防避碰和北斗船位终端的应用，保障渔业生产安全。（牵头单位：省农业农村厅，责任单位：省工业和信息化厅）\n\n**二、加力推进关键领域智慧化建设**\n\n（五）大力发展智能农机装备。加快发展适用于精准作业、果蔬采摘、病虫害巡检、畜禽健康监测、深海养殖监控等复杂场景的智能农机、农业机器人等智能装备。扩大无人机在植保、施肥、农情监测等方面普及应用。鼓励农机制造企业在农机装备出厂前加装北斗导航、传感器等智能化设备，推动农业基础设施与智能农机协同联动。支持“无人农场”建设。（牵头单位：省工业和信息化厅，责任单位：省科技厅、省农业农村厅、省畜牧局）\n\n（六）加快智慧育种技术应用。加大种质资源数据收集与利用力度，建设省级种质资源数据库平台。推动人工智能、大数据等新一代信息技术与传统育种深度融合。支持科研机构和种业企业联合打造智能育种平台，开发智能设计育种工具，推动经验育种向智能设计育种转变，加速育种创新。以寿光、兰陵、莘县、济阳等蔬菜生产大县为重点，推动集约化数字种苗工厂建设。（省发展改革委、省科技厅、省农业农村厅按职责分工负责）\n\n（七）推进农业农村管理服务数字化。建好用好“齐鲁农云”山东省数字农业农村综合管理服务平台，支持开展省市共建试点工作，打造覆盖全省、上下贯通、业务协同的农业农村“数字大脑”。拓展“鲁农码”应用领域，实现涉农业务“一码通行”、服务事项“一码通办”，推进全省农业农村用地“一张图”管理。推动现代农事综合服务中心数字化建设，促进农业社会化服务资源高效整合与供需智能匹配，推动涉农数据价值转化。（省自然资源厅、省农业农村厅、省大数据局按职责分工负责）\n\n（八）加快全产业链数字化改造。充分发挥农业“链主”企业、龙头企业等示范作用，带动产业链上下游各类经营主体进行数字化改造。支持农产品精深加工数字化技术及智能装备研发与应用，推动人工智能技术在农产品分选、加工过程质量控制、供应链协同优化等关键环节的深度融合，建设特色农业“大脑”。积极推动农产品质量安全承诺达标合格证信息化，推进“区块链＋”在农产品质量安全管理中的应用，探索建立食用农产品全链条信息化追溯体系。强化农产品市场监测预警，引导产业有序发展。建好用好“齐鲁农超”平台，加快推动农村电商平台经济高质量发展，不断提升“好品山东 优特农品”品牌影响力。（省发展改革委、省工业和信息化厅、省农业农村厅、省商务厅、省市场监管局、省畜牧局按职责分工负责）\n\n**三、加力推进数字生态效能释放**\n\n（九）强化数据治理。健全农业农村数据管理制度，完善数据资源目录，加强涉农数据治理，建设农业农村高质量数据集。探索构建涉农数据资产价值转化路径，推动数据在农业生产、经营、管理、服务等各环节的应用，充分发挥数据要素价值。搭建安全可信的数据共享与交易环境，构建农业行业可信数据空间，推进网络安全与数据安全防护体系建设。（省委网信办、省农业农村厅、省大数据局按职责分工负责）\n\n（十）强化技术创新。实施“人工智能＋农业”行动，鼓励企业与科研院所、高校联合开展农业传感器与专用芯片、模型算法等研发攻关与成果转化，深入推进人工智能大模型、大数据分析等技术在农业农村领域融合应用，加快构建“空天地一体化”数字农业技术体系。鼓励各地根据发展需求，有针对性地组织开展智慧农业技术研发。强化企业创新主体地位，积极培育智慧农业领军企业，整合科研院所、高校、龙头企业和新型生产经营组织等资源，形成“产学研用”一体化创新模式。（省教育厅、省科技厅、省农业农村厅、省畜牧局按职责分工负责）\n\n（十一）强化技术推广。鼓励涉农高校、科研机构、推广单位与经营主体开展智慧农业技术集成应用示范，实施省农业重大技术协同推广计划，遴选发布一批智慧农业主推技术。加快推进物联网、大数据、人工智能、北斗导航、高分遥感等现代信息技术推广应用。加快制（修）订智慧农业标准规范，建立健全智慧农业技术推广服务体系，加强智慧农业技术指导和推广。鼓励有条件的农业社会化服务主体加快应用智慧农业技术，提供农情遥感监测、农事作业、经营管理、防灾减灾等技术服务。（省教育厅、省科技厅、省农业农村厅、省畜牧局按职责分工负责）\n\n（十二）强化场景应用。开展实数融合促进行动，依托全省农业数字赋能平台等，推进数字农业发展县建设，探索形成区域性的智慧农业解决方案。加快粮食作物、设施园艺、畜牧水产、特色产业及智慧气象、农机服务等重点领域的场景创新与推广，指导各地根据产业发展实际，因地制宜打造一批技术融合度高、应用实效性强、复制推广性好的智慧农业应用场景。到2030年，建设80个数字农业发展县，重点打造300个高水平的智慧农业应用场景，全省农业生产信息化率达到60％以上。（省农业农村厅、省大数据局、省畜牧局、省气象局按职责分工负责）\n\n**四、切实做好组织保障工作**\n\n由农业农村部门牵头，加强与发展改革、财政、科技、工业和信息化、自然资源、畜牧兽医、大数据、气象等部门沟通配合，强化省、市、县三级协同联动，确保任务落到实处。统筹乡村振兴重大专项资金等各级相关领域财政资金推进智慧农业建设，重点支持关键技术攻关、数字农业发展县和应用场景建设。省级每年支持建设30个智慧农业应用场景。优化金融服务落实税收优惠政策，支持智慧农业科技企业和创新平台发展。推动高校、科研院所与智慧农业企业深化合作，培养精通信息技术与农业产业的复合型人才。持续推动高素质农民培育、基层农技推广队伍素质提升等项目，提升生产经营主体数字素养与技能。推进智慧农业领域从业人员职业技能培训与评价，吸引各类人才投身智慧农业发展。（省委金融办、省发展改革委、省教育厅、省科技厅、省工业和信息化厅、省财政厅、省人力资源社会保障厅、省自然资源厅、省农业农村厅、省大数据局、省畜牧局、山东金融监管局、省税务局、省气象局按职责分工负责）\n\n山东省人民政府办公厅\n\n2026年9月18日\n[【打印本页】](javascript:window.print())[【关闭窗口】](javascript:window.opener=null;window.open('','_self');window.close();)","淄博市农业农村局·2026年09月30日","2026-09-30T05:58:48Z","政策",90,{"impact":141,"substance":142,"depth":143,"authority":144,"freshness":90,"relevant":21,"comment":145},26,24,19,13,"省级智慧农业顶层政策文件，条款与量化目标明确，对全国农业信息化建设具有示范参考价值，值得进入每日精选。",[147],{"name":136,"url":133},[26,27,149,150,28,151],"智能农机","齐鲁农云","数字农业发展县",{"code":153,"slug":154,"name":155,"path":156,"parent_code":157},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":160,"locked":14,"version":34,"method":35,"evidence":161},"classified",{"candidates":162,"agricultural_evidence":41},[163,168,174,180,186,192,198,204,210,216,221,225],{"code":59,"score":19,"supported":41,"title":164,"abstract":165,"tags":167},[26],[26,166],"数字农业",[26],{"code":153,"score":20,"supported":41,"title":169,"abstract":170,"tags":173},[],[171,172],"大豆","花生",[],{"code":175,"score":40,"supported":41,"title":176,"abstract":177,"tags":179},"A2",[],[178],"小麦",[],{"code":181,"score":40,"supported":41,"title":182,"abstract":183,"tags":185},"A3",[],[184],"玉米",[],{"code":187,"score":40,"supported":41,"title":188,"abstract":189,"tags":191},"A8",[],[190],"单产提升",[],{"code":193,"score":40,"supported":41,"title":194,"abstract":195,"tags":197},"C",[],[196],"畜牧",[],{"code":199,"score":40,"supported":41,"title":200,"abstract":201,"tags":203},"D",[],[202],"渔业",[],{"code":205,"score":40,"supported":41,"title":206,"abstract":207,"tags":209},"F1",[],[208],"种质资源",[],{"code":211,"score":40,"supported":41,"title":212,"abstract":213,"tags":215},"F3",[],[214],"水肥一体化",[],{"code":217,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},"F5",[],[],[149],{"code":64,"score":21,"supported":14,"title":222,"abstract":223,"tags":224},[],[],[27],{"code":53,"score":21,"supported":14,"title":226,"abstract":227,"tags":228},[],[],[28],[230,231],"山东 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向\"智\"而行——大力发展智慧农业加快建设数字乡村","https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F17\u002Fc_1789925195600209.htm","专家解读指出,\"十五五\"时期数字乡村将加快迈向数智乡村,智慧农业建设将进入创新发展、落地见效的关键阶段。《加快农业农村现代化\"十五五\"规划》明确\"推进人工智能运用和智慧农业发展\"。人工智能等数智技术正加速演进,数据产业加快培育、应用场景全链拓展、新兴产业加快培育,为智慧农业发展带来前所未有的新机遇:数据从辅助性工具发展为新型生产要素,农业大模型与智能装备在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景加速落地。","习近平总书记高度重视数字乡村建设和智慧农业发展，作出重要指示强调，“瞄准农业现代化主攻方向，提高农业生产智能化、经营网络化水平，帮助广大农民增加收入”“要用好现代信息技术，创新乡村治理方式，提高乡村善治水平”。2019年，中共中央办公厅、国务院办公厅印发了《数字乡村发展战略纲要》。此后，中央一号文件连续八年对推进数字乡村和智慧农业作出重要部署。各地区各有关部门持续推进数字技术与农业生产、乡村生活日益融合，数字乡村建设和智慧农业发展取得重要阶段性成效。近日，国家互联网信息办公室、农业农村部联合发布《中国数字乡村发展报告（2019—2025年）》（以下简称《报告》），系统总结了七年来我国数字乡村发展的成就和经验。《报告》立足新形势新要求，展示了以信息基础设施为底座、数据资源体系为核心、智慧农业与乡村数字经济为重点、数字文化与数字治理为支撑、信息服务与智慧美丽乡村为拓展、政策机制与人才队伍为保障的体系化发展路径。该《报告》不仅为全面了解发展成效、科学谋划“十五五”数字乡村发展蓝图提供了重要参考，也积极回应各方关切，向国际社会展示了我国借助数字技术推动农业与乡村治理数智化转型的经验。回顾七年历程，智慧农业作为数字乡村建设的重要内容，已由试点探索转向快速起步、由点状突破迈向系统推进，正乘“数”而上，向“智”而行，为推进农业农村现代化提供有力支撑。\n\n**一、智慧农业正从“盆景”走向“风景”**\n\n数字乡村涵盖乡村经济、治理、文化、服务等多个维度，内涵丰富。《报告》提出，智慧农业是“农业新质生产力的重要内容，是乡村产业数字化的关键着力点”。智慧农业为数字乡村高质量发展提供了坚实的产业支撑，成为推动数字乡村发展的关键动能。《报告》显示，七年的探索推进和快速发展，推动智慧农业实现了“四个跨越”。\n\n**第一，智慧农业基础设施实现从“基础覆盖”到“深化赋能”的跨越。**完善的网络基础设施为智慧农业在田间地头、池塘圈舍的落地拓展提供了基础支撑。截至2025年底，农村地区互联网普及率达69.5%，较2018年底提升31.1个百分点。传统基础设施数字化为智慧农业提供了更加坚实的硬件底座和场景支撑，农村水利、农田、电网、公路及寄递物流等持续升级完善。农业数据资源日益丰富，为智慧农业落地应用提供了基础资源和创新引擎，“天空地一体化”监测网络等新型基础设施加快建设，全国农产品批发市场价格信息等涉农数据开发利用不断深入。\n\n**第二，关键技术装备实现从“基础”到“核心”的跨越。**智能农机装备研发应用取得重要进展，新一代信息技术与农业装备深度融合，正推动农业生产方式从“靠天吃饭”向“知天而作”加速转变。产学研用相衔接的智慧农业创新体系加快形成，支撑取得一批关键智慧农业技术装备创新成果。《报告》显示，截至2025年底，累计建设智慧农业创新中心、分中心34个，智慧农业创新应用项目116个，104项关键智慧农业技术和62项整机智能装备研发取得突破。智慧农业技术装备质量管控更加严格、应用推广不断拓展，布局建设国家农机装备产业计量测试中心，强化农机装备产业计算测试技术研究与应用。\n\n**第三，主要产业数字化实现从“单点试验”到“面上推广”的跨越。**大田种植领域，天空地一体化农情感知与数据驱动模式初步构建，实现水稻、小麦、玉米苗情长势动态监测。截至2025年底，累计推广应用各类农机北斗终端超350万台套，农用无人机保有量超过30万架、年作业面积突破4.6亿亩。智能农机共享租赁加速普及。畜禽养殖领域，精准饲喂、环境控制、行为分析等智能技术广泛应用于生猪养殖和家禽立体高效养殖中。全国659个动物防疫通道纳入信息化管理，动物检疫监督更加智能化、便捷化和高效化。渔业领域，数字技术持续赋能多元化养殖模式，智能化网箱设备、投料机器人等智能装备加速迭代，海洋养殖智能化水平不断提升。\n\n**第四，粮食安全保障实现从“人工管控”到“数智赋能”的跨越。**粮食安全是“国之大者”，数智技术正在为其构筑起坚实保障。在耕地保护方面，“三区三线”等“一张图”相关基础数据库进一步完善，让“藏粮于地”有了更坚实的数据底座，助力守牢18亿亩耕地红线。在种业振兴方面，中国种业大数据平台建成运行，全国农作物种质资源信息平台已上线58.8万份国家级库圃种质资源信息，为育种创新提供了坚实的资源基础。在防灾减损领域，气象预警信息全面接入全国123万个应急广播终端并在16个省份386个市县试行开展“闪信”技术应用，以气象预警为先导的应急响应联动机制更加健全。在仓储方面，借助数字化仓储技术，粮库储粮周期内综合损耗率控制在1%以内，支撑节粮减损效果明显。从种到收、从田间到粮仓，数智技术正在全链条赋能国家粮食安全保障体系。\n\n**二、智慧农业发展需要坚定走好符合国情农情的路子**\n\n七年来，在信息革命加速农业深刻变革的进程中，智慧农业加快发展、数字乡村建设深入推进，推动农业成为更有奔头的产业、农村成为更加宜居宜业的家园，为网络强国、农业强国建设贡献了重要力量。回顾七年实践，我们进一步深化了对智慧农业发展的规律性认识。\n\n**一是政府引导与市场机制协同发力。**党中央、国务院印发的《加快建设农业强国规划（2024—2035年）》、农业农村部印发的《关于大力发展智慧农业的指导意见》《全国智慧农业行动计划（2024—2028年）》等文件构建了智慧农业“四梁八柱”。七年来，从智慧农业创新中心布局到创新应用项目建设实施，从主推技术遴选、典型案例推介到智慧农业创新大赛，政府的“有形之手”在搭建平台、降低门槛、推动产业化等方面发挥了重要作用。同时，平台经济等推动拓展创业空间，返乡青年、家庭农场、农民合作社和农村个体商户以平台化方式进入市场、链接消费和重构经营模式，农村电商、数字服务等新业态加速发展，市场的“无形之手”进一步增强了智慧农业发展的动力活力。\n\n**二是技术创新与农情农艺深度结合。**农业不同于工业、农村不同于城市，发展智慧农业、建设数字乡村必须坚持问题导向、应用导向，走适宜化、低成本、易操作的技术路线。近年来，农机北斗终端实现快速规模化推广，在于其有效契合了播种、收获等关键环节的实际生产需求，让农民“用得上、用得起、用得好”。\n\n**三是智慧农业与小农户有机衔接。**“大国小农”的基本国情农情决定了智慧农业要实现大规模落地应用，必须坚持让小农户共享数字红利的现实路径。各类农业社会化服务组织加速布点，通过集采智能装备、统一调度作业、提供“菜单式”服务，将智能农机、无人机植保、精准施肥等先进技术和装备转化为小农户“点单即享”的标准化服务，有效破解小农户“买不起、用不好”的难题。以社会化服务为纽带，智慧农业正成为促进小农户与现代农业发展有机衔接的重要手段。\n\n**三、奋力推进“十五五”时期智慧农业建设**\n\n“十五五”时期是基本实现农业农村现代化的关键时期。展望未来五年，数字乡村将加快迈向数智乡村，智慧农业建设将进入创新发展、落地见效的关键阶段。《加快农业农村现代化“十五五”规划》明确，要“推进人工智能运用和智慧农业发展”。这要求我们既要总结运用好实践中积累形成的宝贵经验，又要准确把握未来数智技术和农业发展新趋势。\n\n当前，人工智能等数智技术加速演进，深刻重塑农业发展的底层逻辑，为智慧农业发展带来了前所未有的新机遇。**一是数据产业加快培育，释放要素价值潜能。**数据从支撑农业农村发展的辅助性工具，逐步发展为具有独立价值、可市场化运营的新型生产要素，其基础资源和创新引擎作用日渐显现，数智技术加速内化成为农业农村领域的发展动能，要抓实数据这个根本，进一步加快“统筹部署农业农村数据基础设施”“发展农业农村领域数据产业”。**二是应用场景全链拓展，场景驱动成为重要引擎。**农业大模型、智能装备加速在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景落地，场景驱动技术迭代的效能日益凸显，要打造丰富多样的应用场景，“加快农业人工智能应用场景拓展”**。三是新兴产业加快培育，拓展农业发展新空间。**智能设计育种、新能源农机、农业低空经济等先导性产业规模化发展，开辟智慧农业高质量发展全新赛道，要从智能育种等产业急需领域做起，加快“培育发展乡村新产业新业态”。\n\n《报告》的发布既是阶段性总结，更是新征程的动员。面向“十五五”，我们要坚决贯彻党中央、国务院关于大力推进“人工智能+”农业的部署要求，在基础设施上强基固本，在关键技术装备上聚力攻坚，在产业数智化上扩面提效，在粮食安全保障上筑牢数字防线，加快推动智慧农业从“点上突破”迈向“面上成势”，从“量的积累”转向“质的跃升”。以智慧农业的创新发展，推动数字乡村高质量发展，为加快农业农村现代化、扎实推进乡村全面振兴注入更加澎湃的数智动能。（作者：李韶民 农业农村部信息中心副主任）","中央网信办 \u002F 网信中国","2026-09-17T00:00:00Z",25,94,{"impact":245,"substance":142,"depth":18,"authority":19,"freshness":246,"relevant":21,"comment":247},28,9,"中央网信办与农业农村部联合发布七年数字乡村发展报告，含大量权威数据与十五五部署方向，政策层级高、信息增量足，值得进入每日精选。",[249,250],{"name":240,"url":237},{"name":251,"url":252},"天津网信网","https:\u002F\u002Fwww.tjcac.gov.cn\u002Fwxdt\u002Fqg\u002F202609\u002Ft20260922_7380508.html",[254,95,26,27,255,28],"十五五","粮食安全",{"code":53,"slug":257,"name":258,"path":259,"parent_code":260},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":160,"locked":14,"version":34,"method":35,"evidence":263},{"candidates":264,"agricultural_evidence":41},[265,269,274,280,286,292,298,302],{"code":53,"score":144,"supported":41,"title":266,"abstract":267,"tags":268},[95],[95],[28,95],{"code":59,"score":270,"supported":41,"title":271,"abstract":272,"tags":273},12,[26],[26],[26],{"code":64,"score":275,"supported":41,"title":276,"abstract":277,"tags":279},4,[],[278],"大模型",[27],{"code":281,"score":40,"supported":41,"title":282,"abstract":283,"tags":285},"F2",[],[284],"生物育种",[],{"code":287,"score":40,"supported":41,"title":288,"abstract":289,"tags":291},"I3",[],[290],"农情监测",[],{"code":293,"score":40,"supported":41,"title":294,"abstract":295,"tags":297},"J2",[],[296],"农业农村现代化",[],{"code":187,"score":21,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[255],{"code":303,"score":21,"supported":14,"title":304,"abstract":305,"tags":306},"J1",[],[],[254],[308,71],"农业人工智能 农业数据要素 数字乡村 智慧农业","农业人工智能农业数据要素数字乡村智慧农业-2816","2026-09-18T00:03:24.723951Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":8,"source_name":317,"source_url":314,"published_at":318,"category":319,"cover_url":8,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":324,"tags":326,"primary_topic":330,"secondary_topics":335,"classification":340,"search_phrases":357,"slug":360,"view_count":73,"doi":361,"paper":362,"created_at":378},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",69,{"impact":90,"substance":322,"depth":18,"authority":270,"freshness":13,"relevant":21,"comment":323},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[325],{"name":317,"url":314},[26,27,327,328,329],"小农户","植物病害识别","可解释性",{"code":331,"slug":332,"name":333,"path":334,"parent_code":109},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services",[336],{"code":64,"slug":337,"name":338,"path":339,"parent_code":260},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":160,"locked":14,"version":34,"method":35,"evidence":341},{"candidates":342,"agricultural_evidence":41},[343,348,353],{"code":331,"score":270,"supported":41,"title":344,"abstract":346,"tags":347},[345],"smallholder",[345],[327],{"code":64,"score":13,"supported":41,"title":349,"abstract":351,"tags":352},[350],"computer vision",[],[27,328],{"code":59,"score":21,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[26],[358,359],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":361,"openalex_id":363,"authors":364,"venue":317,"cited_by_count":73,"oa_url":314,"card":371,"direction":375,"ingested_from":377},"W7223137323",[365,367,369],{"name":366,"orcid":8},"Akshay Ramgoolam",{"name":368,"orcid":8},"Priya Bundhoo Appadoo",{"name":370,"orcid":8},"Jean-Noël LEspérance",{"tldr":372,"method":373,"finding":374,"direction":375,"opportunity":376},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","农业人工智能与决策模型","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","openalex","2026-10-22T11:21:10.425290Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":8,"source_name":317,"source_url":382,"published_at":318,"category":319,"cover_url":8,"hotness":13,"is_selected":14,"score":320,"score_detail":385,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":399,"classification":401,"search_phrases":426,"slug":429,"view_count":73,"doi":430,"paper":431,"created_at":443},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":90,"substance":17,"depth":18,"authority":270,"freshness":246,"relevant":21,"comment":386},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[388],{"name":317,"url":382},[26,27,390,391,392],"灌溉决策","安全保证","试验设计",{"code":394,"slug":395,"name":396,"path":397,"parent_code":398},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[400],{"code":64,"slug":337,"name":338,"path":339,"parent_code":260},{"status":160,"locked":14,"version":34,"method":35,"evidence":402},{"candidates":403,"agricultural_evidence":41},[404,409,415,422],{"code":394,"score":19,"supported":41,"title":405,"abstract":406,"tags":408},[395],[407,395],"灌溉",[390],{"code":64,"score":270,"supported":41,"title":410,"abstract":412,"tags":414},[411],"artificial intelligence",[413],"decision support",[27],{"code":416,"score":20,"supported":41,"title":417,"abstract":418,"tags":421},"H2",[],[419,420],"土壤","soil",[],{"code":59,"score":21,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[26],[427,428],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":430,"openalex_id":432,"authors":433,"venue":317,"cited_by_count":73,"oa_url":382,"card":438,"direction":375,"ingested_from":377},"W7223131205",[434,436],{"name":435,"orcid":8},"Nassima Benkhelifa",{"name":437,"orcid":8},"Yacine Aït Ahmed",{"tldr":439,"method":440,"finding":441,"direction":375,"opportunity":442},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":8,"source_name":317,"source_url":447,"published_at":318,"category":319,"cover_url":8,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":458,"secondary_topics":459,"classification":461,"search_phrases":476,"slug":479,"view_count":73,"doi":480,"paper":481,"created_at":495},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",70,{"impact":90,"substance":17,"depth":18,"authority":270,"freshness":13,"relevant":21,"comment":452},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[454],{"name":317,"url":447},[26,27,327,456,457],"作物病害识别","模型审计",{"code":331,"slug":332,"name":333,"path":334,"parent_code":109},[460],{"code":64,"slug":337,"name":338,"path":339,"parent_code":260},{"status":160,"locked":14,"version":34,"method":35,"evidence":462},{"candidates":463,"agricultural_evidence":41},[464,468,472],{"code":331,"score":19,"supported":41,"title":465,"abstract":466,"tags":467},[345],[327,345],[327],{"code":64,"score":13,"supported":41,"title":469,"abstract":470,"tags":471},[350],[],[456,27],{"code":59,"score":21,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[26],[477,478],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":480,"openalex_id":482,"authors":483,"venue":317,"cited_by_count":73,"oa_url":447,"card":490,"direction":375,"ingested_from":377},"W7223088030",[484,486,488],{"name":485,"orcid":8},"Abdi-Basid Idriss Farah",{"name":487,"orcid":8},"Amina Abdillahi Guedi",{"name":489,"orcid":8},"Moussa Omar Youssouf",{"tldr":491,"method":492,"finding":493,"direction":375,"opportunity":494},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z"]