[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-6":3,"related-6":60},{"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":21,"tags":23,"primary_topic":28,"secondary_topics":34,"classification":35,"search_phrases":54,"slug":57,"view_count":58,"doi":8,"paper":8,"created_at":59},6,"5G 赋能 AI 耕新！山东移动数智点亮智慧农业新图景","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5328858693_13d9fee4502002f2ym.html","山东移动以 5G 为基、AI 为核、数据为翼，打造\"云—网—边—端—智\"全栈能力，在临沂市沂南县荟馨园农场联合沂南县农业农村局搭建智慧大棚一体化平台。大棚内智能终端密布，24 小时自动采集作物长势等 30 余项数据，通过 5G 网络实时上传至平台；水肥一体机根据作物生长情况精准调配氮磷钾比例，水肥利用率提升 30% 以上、化肥用量减少 25%；虫情监测设备自动识别虫害种类，超标即刻推送预警。山东作为全国重要粮食主产区，山东移动同步在低空智联、农业大模型等领域开展创新实践，推动农业生产从\"靠经验\"向\"靠数据\"、从\"人力忙\"向\"智能管\"转型。",null,"*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   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avatar](http:\u002F\u002Fn.sinaimg.cn\u002Fsinakd10200\u002F360\u002Fw180h180\u002F20200319\u002F61f5-iqyrykv2690431.jpg)\n\n[齐鲁晚报-齐鲁壹点](http:\u002F\u002Fk.sina.com.cn\u002FmediaDocList.d.html?uid=5328858693) 齐鲁晚报官方客户端微博 \n\n[举报](https:\u002F\u002Fnews.sina.com.cn\u002Fblank\u002Freport_news.shtml?docId=nihrxcc2226584 \"打开举报页\")[缩小字体](javascript:; \"减小字号\")[放大字体](javascript:;)[收藏](javascript:; \"收藏本页\")[微博](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https%3A%2F%2Fk.sina.cn%2Farticle_5328858693_13d9fee4502002f2ym.html%3Ffrom&title=5G%E8%B5%8B%E8%83%BD%20AI%E8%80%95%E6%96%B0%EF%BC%81%E5%B1%B1%E4%B8%9C%E7%A7%BB%E5%8A%A8%E6%95%B0%E6%99%BA%E7%82%B9%E4%BA%AE%E6%99%BA%E6%85%A7%E5%86%9C%E4%B8%9A%E6%96%B0%E5%9B%BE%E6%99%AF&pic= \"分享到微博\")[微信](javascript:; \"分享到微信\")[分享](javascript:; \"更多分享\")[](javascript:;)\n\n[腾讯QQ](http:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https%3A%2F%2Fk.sina.cn%2Farticle_5328858693_13d9fee4502002f2ym.html%3Ffrom&desc=&title=5G%E8%B5%8B%E8%83%BD%20AI%E8%80%95%E6%96%B0%EF%BC%81%E5%B1%B1%E4%B8%9C%E7%A7%BB%E5%8A%A8%E6%95%B0%E6%99%BA%E7%82%B9%E4%BA%AE%E6%99%BA%E6%85%A7%E5%86%9C%E4%B8%9A%E6%96%B0%E5%9B%BE%E6%99%AF&summary=&pics=&flash=&site= \"分享到QQ\")[QQ空间](http:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https%3A%2F%2Fk.sina.cn%2Farticle_5328858693_13d9fee4502002f2ym.html%3Ffrom&title=5G%E8%B5%8B%E8%83%BD%20AI%E8%80%95%E6%96%B0%EF%BC%81%E5%B1%B1%E4%B8%9C%E7%A7%BB%E5%8A%A8%E6%95%B0%E6%99%BA%E7%82%B9%E4%BA%AE%E6%99%BA%E6%85%A7%E5%86%9C%E4%B8%9A%E6%96%B0%E5%9B%BE%E6%99%AF&desc=&summary=&pics= \"分享到QQ空间\")\n\n在临沂市沂南县荟馨园农场的大棚里，黄瓜藤蔓爬满支架，翠绿果实挂满枝头，自动灌溉设备精准滴灌，指尖轻点完成大棚管理。5G联网、AI种地、手机管棚的现代农耕新画卷徐徐展开，这也是山东移动以数智力量赋能农业农村现代化的生动实践。\n\n山东作为农业大省、粮食大省，是全国重要的粮食主产区，为助力建设更高水平的“齐鲁粮仓”，山东移动以5G为基、AI为核、数据为翼，打造“云—网—边—端—智”全栈能力，通过智慧大棚、低空智联、农业大模型等创新实践，推动农业生产从“靠经验”向“靠数据”、从“人力忙”向“智能管”转型，为农业农村现代化注入强劲数智动能。\n\n织密“感知网”，让黄瓜“开口说话”\n\n为破解传统种植“看天吃饭”、管理粗放的问题，山东移动联合沂南县农业农村局，为黄瓜示范种植区搭建智慧大棚一体化平台，构建起覆盖全园区的“5G+物联网”感知网络。大棚内智能终端密布，24小时自动采集作物长势等30余项数据，通过5G网络实时上传至平台，打造一张智能的“数字神经网”。\n\n![Image 23](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260713s\u002F65\u002Fw553h312\u002F20260713\u002F095d-9f533c081b2700aecad3aedf35b249c4.png\u002Fw700d1q75cms.jpg?by=cms_fixed_width)\n\n“以前种黄瓜全凭经验，一天跑十几个棚都顾不过来，现在黄瓜自己会‘喊渴’‘喊热’，手机上就能收到提醒。” 种植户刘新庆的感慨，正是平台智能化的直观体现。平台对接AI模型，可根据种植户的咨询自动分析数据并生成专属农事方案，将灌溉、施肥、通风等指令一键推送至种植户手机，实现“数据多跑路、农户少跑腿”。沂南县农业农村局相关负责人表示，智慧大棚一体化平台让种植户在手机上就能实时掌握大棚环境数据，每天轻松管理十几个大棚，AI几分钟即可生成精准农事指令，让田间地头的新质生产力充分释放，彻底改变了传统农业的生产逻辑。\n\n![Image 24](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260713s\u002F65\u002Fw553h312\u002F20260713\u002F4855-93585d9fc0942b537cb566e824422925.png\u002Fw700d1q75cms.jpg?by=cms_fixed_width)\n\n升级“智能装备”，让种植科学高效\n\n从“大水漫灌”到“精准滴灌”，从“见虫打药”到“预警在先”，山东移动不仅让数据“跑起来”，更让农业装备 “聪明”起来。依托5G+物联网、AI及大数据技术，平台联动水肥一体机、智能卷帘机、虫情监测设备等智能终端，实现大棚管理全流程自动化、精准化。\n\n![Image 25](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260713s\u002F65\u002Fw553h312\u002F20260713\u002F11b7-b2ccf7c7f61436dc93d31d278147ff52.png\u002Fw700d1q75cms.jpg?by=cms_fixed_width)\n\n水肥一体机可根据棚内蔬菜生长情况精准调配氮磷钾比例，将水肥直接滴灌至黄瓜根系，水肥利用率提升30%以上，化肥用量减少25%；虫情监测设备自动识别虫害种类，一旦超标即刻推送预警，让种植户提前防控，大幅减少农药使用。种植户只需通过手机 APP，就能远程控制卷帘通风、启动灌溉、调节水肥，即便不在棚内，也能实时掌握作物生长情况。“前几天棚里湿度超标，平台立刻预警，我远程打开通风设备，避免了霜霉病爆发，要是搁以前，一棚瓜早就减产了。” 种植户刘新庆说。\n\n推动绿色转型，让产业提质增效\n\n数字化管控不仅提升了生产效率，更推动沂南黄瓜产业实现绿色转型、高质量发展。智慧大棚一体化平台的精准管控，让水土资源得到高效利用，告别了以往“浇水靠估摸、施肥凭感觉”的粗放模式，节水节肥效果显著。种植户刘新庆坦言，精准滴灌让水和肥料都省了下来，种出的黄瓜达绿色无公害标准，超市、电商争相采购，种植户实打实增收。\n\n![Image 26](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260713s\u002F65\u002Fw553h312\u002F20260713\u002F2493-c2a44cc440d500ce6ac349e9abfc2090.png\u002Fw700d1q75cms.jpg?by=cms_fixed_width)\n\n截至目前，山东移动打造的智慧大棚一体化平台已覆盖沂南县300余个黄瓜示范棚，累计推送温湿度、气象、虫情预警、水肥管理等信息近万条，为沂南黄瓜百亿产业链注入智慧动能。\n\n![Image 27](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260713s\u002F65\u002Fw553h312\u002F20260713\u002F7363-7cf5b6407bff73fd5bf14ba243b65501.png\u002Fw700d1q75cms.jpg?by=cms_fixed_width)\n\n下一步，山东移动将持续拓展智慧农业数字化场景，深化 5G、AI、大数据等技术与农业生产、流通、销售全链条融合，以数智力量赋能绿色蔬菜产业转型，为山东农业农村现代化注入强劲动能，守护好群众的“菜篮子”。\n\n特别声明：以上文章内容仅代表作者本人观点，不代表新浪网观点或立场。如有关于作品内容、版权或其它问题请于作品发表后的30日内与新浪网联系。\n\n[![Image 28: 赛博对话](https:\u002F\u002Fn.sinaimg.cn\u002Fdefault\u002F393\u002Fw253h140\u002F20251230\u002Fd8d0-a01eca45e0968e8dbf7a84cd93d27d89.png)](https:\u002F\u002Fweibo.com\u002Fu\u002F5893229038)\n\n[赛博对话](https:\u002F\u002Fweibo.com\u002Fu\u002F5893229038)\n\n[![Image 29: 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15:19","2026-07-13T07:19:00Z","报道",10,false,32,{"impact":13,"substance":17,"depth":4,"authority":4,"freshness":18,"relevant":19,"comment":20},8,2,1,"地方运营商智慧农业案例报道，内容较泛，时效性差，价值有限。",[22],{"name":10,"url":6},[24,25,26,27],"5G+农业","智慧大棚","农业人工智能","山东",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":36,"locked":14,"version":37,"method":38,"evidence":39},"classified","2026-10-22.2","rules",{"candidates":40,"agricultural_evidence":43},[41,48],{"code":42,"score":17,"supported":43,"title":44,"abstract":46,"tags":47},"I1",true,[45],"智慧农业",[],[],{"code":29,"score":49,"supported":43,"title":50,"abstract":51,"tags":53},4,[],[52],"大模型",[26],[55,56],"农业人工智能 智慧大棚 5G+农业 山东","农业人工智能 智慧大棚","农业人工智能智慧大棚5G+农业山东-6",14,"2026-07-27T05:05:18.613497Z",{"total":4,"page":19,"page_size":4,"items":61},[62,118,161,228,293,344],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":8,"content":67,"source_name":68,"source_url":8,"published_at":69,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":70,"score_detail":71,"sources":77,"tags":79,"primary_topic":82,"secondary_topics":83,"classification":84,"search_phrases":113,"slug":116,"view_count":19,"doi":8,"paper":8,"created_at":117},1164,"山东发布农业产业大模型一期项目 齐鲁农超构建山东农业特色产业大脑","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7678607272322269696\u002F","8月27日,2026山东省农业特色产业品牌推广暨齐鲁农超生态构建推进会在济南举行,现场发布山东农业产业大模型一期项目。山东农业产业大模型依托齐鲁文化大模型算力底座,整合齐鲁农超数据,核心亮点集中体现为四大硬核成果:一张地图(省级农业特色产业数字地图覆盖粮油果蔬畜禽水产全核心品类,汇聚全省100多个重点特色产业集群,精准标注地标农产品产区与重点经营主体近万家)、一条链条(覆盖粮食畜禽果蔬水产的产业链图谱)、一个画像(为涉农企业合作社种养基地建立动态数字档案与智能画像)、一套知识库(汇聚政策标准资讯与产业数据构建知识图谱).平台将持续向山东农业特色产业大脑升级,重点推进四个方面:开发山东农业产业大模型、完善数据底座。","齐鲁晚报·齐鲁壹点 于泊升 边艺 摄影 张中\n\n8月27日，“合作共建 融合赋能”2026山东省农业特色产业品牌推广暨齐鲁农超生态构建推进会在济南举行，活动现场发布了山东农业产业大模型一期项目，展示建设山东农业特色产业大脑的广阔前景。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F0df5ff6cb92c4fe281b9ccce7be512e3~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788825991&x-signature=XEqrH3bqwT3J28zLIqAKCZkiHF4%3D)\n当前大模型技术加速向千行百业渗透，各领域纷纷布局垂直行业大模型，以 AI 赋能产业升级已成为大势所趋。2026年中央一号文件提出“把握智能化发展大势，全方位推进数智技术赋能”。“十五五”规划则明确提出，“发展县域富民产业，推动兴业强县富民一体发展”。山东省数字强省建设、2026年山东省委一号文件中也提到“开展农业领域县域实数融合促进行动”，各项政策持续为农业数字化、智能化蓄势赋能。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fd60a33432df09a579584d70cda007ff2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788825991&x-signature=hSzzwX52y7rWjiz0XPRCp9BkDPw%3D)\n“齐鲁农超”正加快推进山东农业产业大模型建设，可以加快山东农业产业链条长、经营主体分散等痛点解决。\n\n山东农业产业大模型依托齐鲁文化大模型算力底座，整合“齐鲁农超”数据，核心亮点集中体现为“四大硬核成果”。\n\n一张地图，看懂山东“好品”布局。平台搭建省级农业特色产业数字地图，覆盖粮油、果蔬、畜禽、水产全核心品类，汇聚全省100多个重点特色产业集群，精准标注地标农产品产区与重点经营主体近万家。依托省市县三级数字联动体系，实现产业布局可视化、产区资源精准化、市场主体可溯源。\n\n一条链条，透视产业价值跃升。平台已构建覆盖粮食、畜禽、果蔬、水产等产业的产业链图谱，企业在哪里、链条怎么延、价值如何增长，一目了然。\n\n一个画像，打通产销对接闭环。平台为涉农企业、合作社、种养基地建立动态数字档案与智能画像，无缝联动“齐鲁农超”交易体系，实现“看图找主体、点击拓市场、线上即交易”，真正实现数据赋能产销、流量转化销量。\n\n一套知识库，让大模型真懂农业。平台搭建农业知识库，汇聚政策、标准、资讯与产业数据，构建知识图谱，对着地图提问即刻应答。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F6b1567f0ea2fbfc22e2ab6c587dcbbbf~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788825991&x-signature=N1F9RVE1q9OblSFZxOEBxnwq4EY%3D)\n下一步，平台将持续向“山东农业特色产业大脑”升级，重点推进四个方面：\n\n**持续开发山东农业产业大模型**，以齐鲁文化大模型为底座，重点开发山东精品农产品数据集和农业垂类模型，打造贴合山东实际的专属产业大模型；\n\n**持续完善数据底座**，重点补充各类动态数据，深化校企、政企共建共享，建立动态更新长效机制；\n\n**构建多方协同发展生态**，深化产学研协同创新，推动成果落地转化，拓展合作资源，构建共建共享的农业AI服务生态；\n\n**常态化筑牢安全底线**，健全数据安全、模型安全、内容安全等安全制度，常态化开展合规检查与运维，保障大模型规范安全运行。\n\n新闻线索报料通道：应用市场下载“齐鲁壹点”APP，或搜索微信小程序“齐鲁壹点”，全省800位记者在线等你来报料！","齐鲁晚报·齐壹点 2026-08-27","2026-08-27T07:18:00Z",70,{"impact":72,"substance":73,"depth":74,"authority":13,"freshness":75,"relevant":19,"comment":76},22,20,15,3,"省级农业产业大模型项目发布，内容详实，但时效性稍差。",[78],{"name":68,"url":65},[80,26,27,81],"数字乡村","产业大脑",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":85},{"candidates":86,"agricultural_evidence":43},[87,92,98,104,108],{"code":29,"score":88,"supported":43,"title":89,"abstract":90,"tags":91},12,[52],[52],[26],{"code":93,"score":75,"supported":43,"title":94,"abstract":95,"tags":97},"C",[],[96],"畜禽",[],{"code":99,"score":75,"supported":43,"title":100,"abstract":101,"tags":103},"D",[],[102],"水产",[],{"code":42,"score":19,"supported":14,"title":105,"abstract":106,"tags":107},[],[],[81],{"code":109,"score":19,"supported":14,"title":110,"abstract":111,"tags":112},"I6",[],[],[80],[114,115],"农业人工智能 产业大脑 数字乡村 山东","农业人工智能 产业大脑","农业人工智能产业大脑数字乡村山东-1164","2026-09-01T00:03:36.534600Z",{"id":119,"title":120,"url":121,"summary":122,"summary_zh":8,"content":123,"source_name":124,"source_url":8,"published_at":125,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":137,"classification":138,"search_phrases":156,"slug":159,"view_count":142,"doi":8,"paper":8,"created_at":160},126,"AI+农业如何从云上落到地里——从\"会养\"\"会种\"到\"慧养\"\"慧种\",2025年农业人工智能Token年度消耗超2.4亿","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-07-27\u002Fdoc-inikekzt3337234.shtml","新华社报道:人工智能正深度融入农田,从温室喷雾机器人、牛脸识别、北大荒多光谱无人机AI查苗到河北卢龙78座高标准智能温室蓝莓基地,各地探索\"慧养\"\"慧种\"。2025年中国智慧农业市场规模从2017年384.74亿元增至1200亿元,年复合增长率15.28%。但产业仍面临三重短板:一是初始投入大(智慧大棚每平方米300-1500元,种植集装箱20-30万元);二是农村主力为中老年群体,数字化操作能力不足、青年人才短缺;三是山区丘陵复杂地形数据与算法迁移困难,许多AI应用仍停留在监测识别预警阶段。寿光\"AI机器人银发专家\"将40年寿光模式经验提炼为数智化内核,技术服务效率提升5倍以上,业内认为\"让技术找人\"比\"让人学技术\"更易实现。","内蒙古自治区和林格尔县的蒙牛嘉立荷生态牧场，和江苏连云港的东旺奶牛养殖基地，正运行着同一套“牛脸识别”系统。无需近距离拍摄，后台屏幕动态框出牛脸，饮食、健康、行为数据一目了然。\n\n“这不是保险理赔用的识别工具，而是智慧养殖工具。”系统研发方寿光市翔天农业科技有限公司副总经理刘守城说。每头牛都有“数字身份证”，全天候“点名”，可实现精确投喂。养殖数据直传农业、防疫部门，食品安全可追溯。试点以来，已覆盖超7000头牛，饲料利用率提升25%，发病率降低20%。","新华社\u002F兵团日报 2026-07-27","2026-07-27T00:00:00Z",60,{"impact":128,"substance":129,"depth":88,"authority":88,"freshness":18,"relevant":19,"comment":130},18,16,"报道了AI在奶牛养殖中的实际应用，有具体数据和案例，但时效性较差，且为地方性进展。",[132],{"name":124,"url":121},[45,25,26,134,135],"智慧养殖","寿光模式",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":139},{"candidates":140,"agricultural_evidence":43},[141,146,152],{"code":29,"score":142,"supported":43,"title":143,"abstract":144,"tags":145},9,[26],[],[26],{"code":147,"score":49,"supported":43,"title":148,"abstract":149,"tags":151},"B7",[],[150],"温室",[25],{"code":42,"score":49,"supported":43,"title":153,"abstract":154,"tags":155},[],[45],[45],[157,158],"农业人工智能 寿光模式 智慧养殖 智慧农业","农业人工智能 寿光模式","农业人工智能寿光模式智慧养殖智慧农业-126","2026-08-01T06:01:52.387430Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":8,"source_name":167,"source_url":164,"published_at":168,"category":169,"cover_url":8,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":174,"tags":176,"primary_topic":180,"secondary_topics":186,"classification":188,"search_phrases":205,"slug":208,"view_count":209,"doi":210,"paper":211,"created_at":227},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":17,"substance":172,"depth":128,"authority":88,"freshness":13,"relevant":19,"comment":173},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。",[175],{"name":167,"url":164},[45,26,177,178,179],"小农户","植物病害识别","可解释性",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[187],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":36,"locked":14,"version":37,"method":38,"evidence":189},{"candidates":190,"agricultural_evidence":43},[191,196,201],{"code":181,"score":88,"supported":43,"title":192,"abstract":194,"tags":195},[193],"smallholder",[193],[177],{"code":29,"score":13,"supported":43,"title":197,"abstract":199,"tags":200},[198],"computer vision",[],[26,178],{"code":42,"score":19,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[45],[206,207],"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",0,"10.5281\u002Fzenodo.23545193",{"doi":210,"openalex_id":212,"authors":213,"venue":167,"cited_by_count":209,"oa_url":164,"card":220,"direction":224,"ingested_from":226},"W7223137323",[214,216,218],{"name":215,"orcid":8},"Akshay Ramgoolam",{"name":217,"orcid":8},"Priya Bundhoo Appadoo",{"name":219,"orcid":8},"Jean-Noël LEspérance",{"tldr":221,"method":222,"finding":223,"direction":224,"opportunity":225},"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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":167,"source_url":231,"published_at":168,"category":169,"cover_url":8,"hotness":13,"is_selected":14,"score":170,"score_detail":234,"sources":236,"tags":238,"primary_topic":242,"secondary_topics":248,"classification":250,"search_phrases":275,"slug":278,"view_count":209,"doi":279,"paper":280,"created_at":292},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":17,"substance":72,"depth":128,"authority":88,"freshness":142,"relevant":19,"comment":235},"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。",[237],{"name":167,"url":231},[45,26,239,240,241],"灌溉决策","安全保证","试验设计",{"code":243,"slug":244,"name":245,"path":246,"parent_code":247},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[249],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":36,"locked":14,"version":37,"method":38,"evidence":251},{"candidates":252,"agricultural_evidence":43},[253,258,264,271],{"code":243,"score":74,"supported":43,"title":254,"abstract":255,"tags":257},[244],[256,244],"灌溉",[239],{"code":29,"score":88,"supported":43,"title":259,"abstract":261,"tags":263},[260],"artificial intelligence",[262],"decision support",[26],{"code":265,"score":4,"supported":43,"title":266,"abstract":267,"tags":270},"H2",[],[268,269],"土壤","soil",[],{"code":42,"score":19,"supported":14,"title":272,"abstract":273,"tags":274},[],[],[45],[276,277],"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":279,"openalex_id":281,"authors":282,"venue":167,"cited_by_count":209,"oa_url":231,"card":287,"direction":224,"ingested_from":226},"W7223131205",[283,285],{"name":284,"orcid":8},"Nassima Benkhelifa",{"name":286,"orcid":8},"Yacine Aït Ahmed",{"tldr":288,"method":289,"finding":290,"direction":224,"opportunity":291},"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":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":167,"source_url":296,"published_at":168,"category":169,"cover_url":8,"hotness":13,"is_selected":14,"score":70,"score_detail":299,"sources":301,"tags":303,"primary_topic":306,"secondary_topics":307,"classification":309,"search_phrases":324,"slug":327,"view_count":209,"doi":328,"paper":329,"created_at":343},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。",{"impact":17,"substance":72,"depth":128,"authority":88,"freshness":13,"relevant":19,"comment":300},"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。",[302],{"name":167,"url":296},[45,26,177,304,305],"作物病害识别","模型审计",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},[308],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":36,"locked":14,"version":37,"method":38,"evidence":310},{"candidates":311,"agricultural_evidence":43},[312,316,320],{"code":181,"score":74,"supported":43,"title":313,"abstract":314,"tags":315},[193],[177,193],[177],{"code":29,"score":13,"supported":43,"title":317,"abstract":318,"tags":319},[198],[],[304,26],{"code":42,"score":19,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[45],[325,326],"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":328,"openalex_id":330,"authors":331,"venue":167,"cited_by_count":209,"oa_url":296,"card":338,"direction":224,"ingested_from":226},"W7223088030",[332,334,336],{"name":333,"orcid":8},"Abdi-Basid Idriss Farah",{"name":335,"orcid":8},"Amina Abdillahi Guedi",{"name":337,"orcid":8},"Moussa Omar Youssouf",{"tldr":339,"method":340,"finding":341,"direction":224,"opportunity":342},"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",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":8,"source_name":167,"source_url":347,"published_at":168,"category":169,"cover_url":8,"hotness":13,"is_selected":14,"score":350,"score_detail":351,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":360,"classification":361,"search_phrases":380,"slug":383,"view_count":209,"doi":384,"paper":385,"created_at":395},5589,"Conformal-Risk Evaluation for Artificial Intelligence Agriculture Spray-Nozzle Fault Detection Under Label-Delay Feedback: A Computer Coverage Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544107","Spray-nozzle fault detection in precision agriculture is a classification problem in which the label that confirms a fault arrives after the spray pass, so the detector must act on unlabelled evidence and its error rate must be certified before the label exists. Conformal prediction offers distribution-free coverage guarantees, but the guarantee is stated for exchangeable calibration and test data, an assumption that label-delayed feedback violates because the calibration set is always older than the deployment set. the present analysis formalises label-delay feedback as a distribution shift in the conformity score and derives a coverage bound for split-conformal fault detectors under that shift. The bound separates the nominal miscoverage rate into an exchangeable component and a delay-induced component that scales with the total variation distance between the calibration and deployment score distributions. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. The estimator recovered the declared coefficients with estimated bias 0.01122 for the intercept and -0.0002 for exposure, root mean squared error 0.9217 and 0.01614 respectively, and empirical coverage of the nominal 95% intervals of 1.0 for both parameters across 600 replications. The coverage audit shows that the delay-induced term, not the exchangeable term, dominates the risk of a conformal spray-nozzle detector, and that the audit procedure can certify a detector before field labels return.","在精准农业中，喷雾喷嘴故障检测是一个分类问题，其中确认故障的标签在喷雾作业结束后才到达，因此检测器必须基于未标记的证据采取行动，并且其错误率必须在标签存在之前得到认证。保形预测（conformal prediction）提供了无分布覆盖保证，但该保证是针对可交换的校准数据和测试数据而言的，而标签延迟反馈违反了这一假设，因为校准集总是比部署集更旧。本分析将标签延迟反馈形式化为一致性分数中的分布偏移，并推导了在该偏移下分裂保形故障检测器的覆盖界。该界将名义错误覆盖 rate 分解为可交换分量和延迟诱导分量，后者随校准与部署分数分布之间的全变差距离而缩放。对 ['Intercept', 'exposure', 'group', 'severity'] 估计器进行了基于重复的评估：从声明的数据生成过程（截距 12.0，暴露 0.42，分组 -3.1，严重程度 1.35，误差标准差 4.2）中抽取了 600 个 N = 500 的数据集，研究刻画了普通最小二乘估计器在所述过程下的行为。该估计器恢复了声明的系数，截距的估计偏差为 0.01122，暴露为 -0.0002，均方根误差分别为 0.9217 和 0.01614，两个参数在 600 次重复中名义 95% 区间的经验覆盖率为 1.0。覆盖审计表明，延迟诱导项而非可交换项主导了保形喷雾喷嘴检测器的风险，并且该审计程序可以在田间标签返回之前对检测器进行认证。",62,{"impact":17,"substance":128,"depth":352,"authority":142,"freshness":13,"relevant":19,"comment":353},17,"论文提出标签延迟下共形预测覆盖界，方法有新意但属预印本、应用面窄，可作为专业补充而非每日精选头条。",[355],{"name":167,"url":347},[45,26,357,358],"精准施药","病虫害监测",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":362},{"candidates":363,"agricultural_evidence":43},[364,368,375],{"code":29,"score":142,"supported":43,"title":365,"abstract":366,"tags":367},[260],[],[26],{"code":42,"score":369,"supported":43,"title":370,"abstract":371,"tags":374},7,[],[372,373],"精准农业","precision agriculture",[45],{"code":376,"score":19,"supported":14,"title":377,"abstract":378,"tags":379},"F4",[],[],[357],[381,382],"农业人工智能 病虫害监测 智慧农业 精准施药","农业人工智能 病虫害监测","农业人工智能病虫害监测智慧农业精准施药-5589","10.5281\u002Fzenodo.23544107",{"doi":384,"openalex_id":386,"authors":387,"venue":167,"cited_by_count":209,"oa_url":347,"card":390,"direction":224,"ingested_from":226},"W7223116087",[388],{"name":389,"orcid":8},"Achieng' Atim Okello",{"tldr":391,"method":392,"finding":393,"direction":224,"opportunity":394},"将标签延迟反馈建模为共形分数分布偏移，推导覆盖率界并审计喷雾喷嘴故障检测器。","分裂共形预测、总变差距离、600次重复模拟（N=500）评估OLS估计。","延迟诱导项而非可交换项主导检测器风险，审计可在田间标签返回前认证检测器。","可将该覆盖率审计框架扩展到其他标签延迟的农业传感器故障检测，并研究延迟分布估计误差对认证的影响。","2026-10-22T11:21:02.845718Z"]