[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1562":3,"related-1562":69},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},1562,"江苏省农业科学院装备所：光伏慧农、瑞雪地膜、豆好智能体——近 20 个垂直大模型加速田间落地","https:\u002F\u002Ffinance.sina.cn\u002F2026-09-02\u002Fdetail-iniqkkph4632699.d.html","江苏省农科院已涌现近 20 个农业大模型及智能体投入研发或应用，数量和密度居全国前列。新能源设施农业创新团队的\"光伏慧农\"模型可在不同地区、不同作物条件下输出光伏板倾斜角度、板间距，并平衡粮食产量与发电收益。农用新材料创新团队的\"瑞雪\"大模型整合全国 2800 余个县域的长时序气象与土壤数据，是全球地膜领域首个垂直大语言模型。经济作物研究所联合浙江大学共同研发的\"豆好\"智能体已发布。国家食用豆产业技术体系首席科学家、省农科院副院长陈新表示，育种时间可缩短到两三年。",null,"[![Image 2](blob:http:\u002F\u002Flocalhost\u002F51baf7b72fe6cdac85d933fb68bb7570)**打开新浪新闻 发现更多精彩** 打开](javascript:void(0);)\n\n[](https:\u002F\u002Fsina.cn\u002F?vt=4 \"手机新浪网\")\n## [新浪财经](https:\u002F\u002Ffinance.sina.cn\u002F?vt=4 \"新浪财经\")\n\n[](https:\u002F\u002Fsina.cn\u002FIndex\u002Fnav?vt=4 \"网站导航\")[![Image 3](blob:http:\u002F\u002Flocalhost\u002Fa60b3ebabb24ca182698fd8650939101)](https:\u002F\u002Fmy.sina.cn\u002F?vt=4)\n# 给土地装上“最强大脑”，种田越来越省心\n\n滚动播报 09.02 04:08\n（来源：新华日报）\n\n□ 本报记者 张宣 实习生 王徐霖\n\n过去，育种专家寻找一个优良基因，要在海量资料和多年试验中反复比对；农户面对田里的异常状况，更多依靠经验判断。如今，人工智能正在成为农业科研和生产的新助手。\n\n近日，记者走访我省科研院所和农业科技企业发现，一批深耕细分领域的农业垂直大模型正快速落地，覆盖种植、养殖、育种、全链管理等环节，为传统农业装上数字化“大脑”。\n\n据不完全统计，江苏目前已有近20个农业大模型及智能体投入研发或应用，数量和密度居全国前列。然而，要真正服务万千农户，仍需跨越数据、成本、门槛、装备等多道坎。\n\n大模型走进田间棚舍\n\n在大模型操作界面，选择地区盐城市射阳县，选定农作物小麦，短短几秒，一套完整的光伏高效发电方案跃然屏幕：光伏板倾斜多少角度、板与板之间留出多大距离，同时算出小麦大概产量、光伏发电收益以及整体经济效益。8月31日，在江苏省农业科学院农业设施与装备研究所实验室内，新能源设施农业创新团队负责人鲍恩财副研究员向记者演示“光伏慧农”模型。“过去在田地里安装光伏发电板之前，工程师和农业技术人员得反复开会讨论，全凭经验估摸着调整，不仅费时，还很难把种地和发电两方面兼顾好。”鲍恩财说，这套模型不会一味偏向发电，也不是只顾庄稼，而是帮两者找一个最合适的平衡点，项目建设方在动工之前就能看得明明白白：多发一点电，粮食产量会少多少；想要保障收成，发电收益又会损失多少。\n\n在江苏省农科院农用新材料创新团队，“瑞雪”大模型把智能方案延伸到了地膜领域。团队负责人徐磊研究员介绍，长期以来，普通塑料地膜带来的白色污染是农业绿色发展的痛点，降解地膜又因各地气候、土壤差异大，基层很难精准选型。“瑞雪”作为全球地膜领域首个垂直大语言模型，整合全国2800余个县域的长时序气象与土壤数据，输入县域和作物就能输出适配的材料配方、最佳厚度和全周期性能预测，实现“一区一膜、一作一策”，不用再靠经验凭感觉选地膜。\n\n育种赛道上，由江苏省农科院联合浙江大学共同研发的“豆好”智能体于近日发布。江苏省农科院经济作物研究所豆类作物创新团队首席助理、研究员袁星星介绍，1500份绿豆种质数据收集历时六年，可靠性很强。国家食用豆产业技术体系首席科学家、省农科院副院长陈新表示，借助这套模型可以少走育种弯路，将育种时间缩短到两三年。\n\n养殖场里，大模型正在替人巡棚舍。8月30日，在邳州市燕子埠镇一处规模化笼养肉鸭场，一阵警报声响起，几个标着红色预警的笼位号出现在显示器上，提示雏鸭是否死亡。江苏省农业科学院农业设施与装备研究所副所长柏宗春研究员给记者算了一笔账：传统模式下，一栋饲养3万只肉鸭的标准鸭舍，工作人员每天早中晚都要巡棚，每次用时45到60分钟，一天下来光巡查就要三个小时。应用这套模型后，一轮巡查可以缩短一个小时，还能精准定位异常位置，有四栋鸭舍的养殖场一天能省出一个人的全天工作量。\n\n田埂上，智慧工具正在服务万千农户。苏州吴江的种田大户老夏举着手机，对着自家田块拍了两张照片，上传到“作物云”小程序，没过几分钟，系统就完成了作物识别和面积测算，原本要半个月才能办完的农业补贴申报，在家门口就搞定了。这个小程序背后，是中科禾信遥感科技（苏州）有限公司自主研发的金垄大模型。该公司总经理陆洲告诉记者，平台利用人工智能技术与卫星遥感影像协同分析，除了补贴申报，还能精准给出灌溉、施肥、病虫害防治的建议，目前已在苏州全市推广。\n\n百模竞发的底气在哪\n\n把视野放到全国，农业大模型的赛道上百舸争流。中国农业大学“神农大模型4.0”覆盖全国31个省区市，服务百万农户，落地应用超500万亩农田。上海推出国内首个生物育种大模型“丰登”。安徽农业大学发布大豆垂直大模型“丰菽”2.0和梨产业“棃想”大模型。\n\n与这些省份相比，江苏的差异化优势十分鲜明。“‘神农’走的是通用底座路线，‘龙江农知’打的是区域特色牌，江苏选择的则是‘垂直深耕加产业落地’的路径。”江苏省农学会专家表示，我省不追求一个大模型包打天下，而是针对地膜、绿豆、肉鸭、光伏农业、澳龙养殖等极其细分的场景，一个个啃硬骨头。比如全球地膜领域首个垂直大语言模型“瑞雪”、业界首个绿豆智能体“豆好”、江苏省首款通过国家备案的农业垂直大模型“谷语”、国内首个落地农业生产决策的大模型“新犁”，这些“首个”的背后，是江苏科研力量和产业生态的厚积薄发。\n\n江苏农业垂直大模型“百模竞发”的底气还来自更多方面。首先是科研力量雄厚。南京农业大学今年1月发布国内首个面向通用农业领域的开源垂直大语言模型“司农”，构建起总量超40亿token的农业基础数据集。随后又发布世界首个开放式梨行业大语言模型“问梨”和面向农机装备的“司农智机”；江苏省农科院在光伏农业、地膜、绿豆育种、水禽养殖等方向多点突破，形成了完整的垂直模型矩阵。其次是产业基础扎实，苏州已形成人工智能全产业链体系，江苏数字经济核心产业增加值占GDP比重超11%，居全国第二。最后是农业底子好。江苏农业科技进步贡献率超72%，农作物耕种收综合机械化率约90%，两项指标均高出全国平均水平10个百分点，为大模型落地提供了难得的规模化、标准化场景。\n\n要“接地气”还得迈过几道坎\n\n然而热闹的表象之下，农业大模型“不接地气”的问题依然突出。记者在采访中发现，当前不少农业大模型还停留在实验室和示范基地里，真正被普通农户日常使用的并不多。用业内人士的话说，“只是盆景，还不是风景”。\n\n“第一道坎是数据。”专家解释说，大模型要有海量优质数据支持，但农业面对的是分散的主体、细碎的地块、琐碎的农事，土壤、气象、种业等数据分属不同平台，没有统一标准，“数据孤岛”现象普遍。小农户田间真实样本短缺，有些算法只适配平整大田，山区、零散地块精准度大打折扣。\n\n第二道坎是成本。“大型智能设备投入成本高，农业周期长、比较效益低，短期难形成完整商业模式，适合散户的低价简易产品供给不足。”专家表示。\n\n第三道坎是“使用门槛”，农村老人数字操作能力弱，基层缺少既懂农业又懂技术的复合型人才。“一开始说大模型，农户们听不懂，我们就说这是个智能助手，有啥不懂的就问，手把手教农民用一次两次，他们就学会了。”中国农业大学王耀君说，这种基于人际信任的技术扩散，远比任何广告都有效。\n\n最后一道坎是装备。农业很多领域连机械化都没完全实现，除农业无人机领跑全球外，其他智慧农机仍处于起步阶段。人工智能不是万能灵药，不能脱离物联网、大数据、智能装备单独作用于农业。\n\n如何让“最强大脑”真正接上“地气”？江苏正在探索答案。苏州今年8月发布三项“人工智能加农业”团体标准，覆盖水稻苗情遥感、养殖水质监测、设施园艺数据采集，从标准层面破解数据采集不统一的难题。苏州还全面推行“揭榜挂帅”机制，让企业和科研团队围绕真实农业场景攻关，瞄准低成本、易操作，降低使用门槛。\n\n海安发布智慧农业三年行动计划，明确到2028年农业生产信息化率提升至65%，打造千亩级无人农场。扬州瑞丰信息的做法是把知识库做深做透，“这个知识库不是从网上扒的，是我们跟农技专家按照品种和区域磨出来的。”企业负责人丁永华说，每一条知识都经过专家验证，这样农户用起来才放心。\n\n受访专家表示，农业人工智能的生命力在于扎根实际场景，持续积累本地化数据，不断优化迭代。要以真实场景为目标，加速技术装备熟化，培育领军企业，引导科研与制造合作，多方协作、长期投入。同时也要清醒认识到，农业是慢变量，说人工智能重塑农业也好，变革农业也罢，都不能浮躁，更不能一哄而起、一哄而散。\n\n体验完实验室里的科研模型和田埂上的手机应用，记者直观感受到，江苏农业大模型的应用范围越来越广，这些大模型不是飘在天上的概念，而是实实在在解决种地、养殖具体麻烦的生产工具。装上“最强大脑”的土地，未来还会孕育出更多产业发展的新可能。但要让盆景真正变成风景，需要脚踏实地、久久为功。\n\n![Image 4](https:\u002F\u002Fn.sinaimg.cn\u002Fdefault\u002F2fb77759\u002F20151125\u002F320X320.png)\n\n打开新浪新闻\n\n## 澳大利亚S&P\u002FASX 200指数早盘上涨0.3%，报9,050.30点。\n\n## 美元\u002F日元下跌0.32%，报155.305。\n\n## 【华泰证券：建议沿着高景气、低拥挤度、政策边际催化等寻找结构性机会】华泰证券研报称，全球低利率范式正被打破，储蓄供给收缩叠加政府与AI同时加杠杆，资金失衡推动长端利率易上难下。储蓄端，中日欧老龄化、地缘阻碍美元回流、高通胀约束央行扩表、风险偏好压低储蓄率，四重变化使储蓄从充裕走向干涸；需求端，美国高利率高赤字高杠杆陷入债务膨胀循环，科技巨头发债已与国债净发行进入同一数量级，AI融资对美债的挤出开始显现。风险上关注2027年初债务上限、基差交易等脆弱点及AI收入放缓后的融资风险。高利率下资产表现分化，黄金和科技股构成核心哑铃组合，分别押注融资风险暴露与AI叙事兑现，低杠杆强现金流与资源品提供防御。策略上，短期主线不清晰，建议沿着高景气+低拥挤度+政策边际催化等寻找结构性机会。\n\n## 澳大利亚S&P\u002FASX 200指数早盘上涨0.3%，报9,050.30点。\n\n## 美元\u002F日元下跌0.32%，报155.305。\n\n## 相关视频\n\n[![Image 5](https:\u002F\u002Fz0.sinaimg.cn\u002Fauto\u002Fcrop?img=https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20260903ac\u002F527\u002Fw480h847\u002F20260903\u002F10e0-c3461a7d70398f9027b014fde9b9330d.jpg&size=370_207&bgf=1&bgc=%23000000) **仁东村的振兴密码**52次播放 > 01:37](javascript:void(0))\n\n[![Image 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36](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd10217\u002F360\u002Fw180h180\u002F20250221\u002F110e-a1b6c68cbfc152e83787ec80c6115a81.jpg)雕刻时光_Meko](javascript:void(0);)## [绑架姆巴佩！足坛巨星绑票案结局大不同 > _8_新浪体育讯 ![Image 37: 绑架姆巴佩！足坛巨星绑票案结局大不同](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsports\u002Ftransform\u002F326\u002Fw650h476\u002F20260903\u002Ff55b-5ccfb0f05ba48303a0610f8a64b4445f.png\u002Fw230h154f1t0l50q90c55.jpg)](javascript:void(0);)## [美媒曾哀叹：特朗普最愚蠢之处，就是把中美平起平坐的事实说出来 > _8_铅笔如有神助 ![Image 38: 美媒曾哀叹：特朗普最愚蠢之处，就是把中美平起平坐的事实说出来](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260830s\u002F320\u002Fw1200h720\u002F20260830\u002F4922-cc12df8eaa79b539f063c4d72ffe3e27.jpg\u002Fw230h154f1t0l50q90c55.jpg)](javascript:void(0);)### [冲突升级？朝鲜部队已到位，全世界都领教到了，这个小国的厉害！ ![Image 39: 冲突升级？朝鲜部队已到位，全世界都领教到了，这个小国的厉害！](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260901s\u002F211\u002Fw1200h611\u002F20260901\u002F0508-99cf1f5b69e7a289807f3de99e5a805d.jpg\u002Fw230h154f1t0l50q90c55.jpg)![Image 40: 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[按职务他只能当少将，陈毅不满：他劳苦功高，应评中将！ ![Image 55: 按职务他只能当少将，陈毅不满：他劳苦功高，应评中将！](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260721s\u002F416\u002Fw998h1018\u002F20260721\u002Fc29a-33311ecbfb6d1029398ab9ac6d747822.png\u002Fw230h154f1t0l50q90c55.jpg)![Image 56: 按职务他只能当少将，陈毅不满：他劳苦功高，应评中将！](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260721s\u002F358\u002Fw774h384\u002F20260721\u002Fce20-f36aac9f52cc2932025332f984023a4e.png\u002Fw230h154f1t0l50q90c55.jpg)![Image 57: 按职务他只能当少将，陈毅不满：他劳苦功高，应评中将！](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Fsinakd20260721s\u002F377\u002Fw701h476\u002F20260721\u002F0697-d9836265aa8876670d6ac9d026255916.png\u002Fw230h154f1t0l50q90c55.jpg) > _6_豆豆君的纸飞机](javascript:void(0);)\n\n加载中...[视频](https:\u002F\u002Fvideo.sina.cn\u002F?vt=4 \"视频\")[直播](https:\u002F\u002Fbn.sina.cn\u002F?vt=4 \"直播\")[美图](https:\u002F\u002Fphoto.sina.cn\u002F?vt=4 \"美图\")[博客](https:\u002F\u002Fblog.sina.cn\u002F?vt=4 \"博客\")[看点](https:\u002F\u002Fk.sina.cn\u002F?vt=4 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2026-09-02",73,{"impact":18,"substance":80,"depth":81,"authority":20,"freshness":82,"relevant":22,"comment":83},20,15,5,"报道江苏农业大模型研发应用进展，信息具体，省级媒体信源，时效稍逊。",[85],{"name":77,"url":75},[27,28,87,29],"农业大模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":91},{"candidates":92,"agricultural_evidence":46},[93,98,104,108],{"code":32,"score":94,"supported":46,"title":95,"abstract":96,"tags":97},13,[48],[48],[28,87],{"code":99,"score":53,"supported":46,"title":100,"abstract":101,"tags":103},"A2",[],[102],"小麦",[],{"code":52,"score":53,"supported":46,"title":105,"abstract":106,"tags":107},[],[56],[],{"code":59,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},[],[],[27],[113,114],"农业人工智能 农业大模型 智慧农业 江苏","农业人工智能 农业大模型","农业人工智能农业大模型智慧农业江苏-1860","2026-09-08T00:07:13.454040Z",{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":8,"source_name":123,"source_url":120,"published_at":124,"category":125,"cover_url":8,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":131,"tags":133,"primary_topic":137,"secondary_topics":143,"classification":145,"search_phrases":162,"slug":165,"view_count":166,"doi":167,"paper":168,"created_at":184},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":128,"substance":129,"depth":19,"authority":45,"freshness":13,"relevant":22,"comment":130},8,21,"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。",[132],{"name":123,"url":120},[27,28,134,135,136],"小农户","植物病害识别","可解释性",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[144],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":146},{"candidates":147,"agricultural_evidence":46},[148,153,158],{"code":138,"score":45,"supported":46,"title":149,"abstract":151,"tags":152},[150],"smallholder",[150],[134],{"code":32,"score":13,"supported":46,"title":154,"abstract":156,"tags":157},[155],"computer vision",[],[28,135],{"code":59,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],[163,164],"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":167,"openalex_id":169,"authors":170,"venue":123,"cited_by_count":166,"oa_url":120,"card":177,"direction":181,"ingested_from":183},"W7223137323",[171,173,175],{"name":172,"orcid":8},"Akshay Ramgoolam",{"name":174,"orcid":8},"Priya Bundhoo Appadoo",{"name":176,"orcid":8},"Jean-Noël LEspérance",{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"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":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":8,"source_name":123,"source_url":188,"published_at":124,"category":125,"cover_url":8,"hotness":13,"is_selected":14,"score":126,"score_detail":191,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":206,"classification":208,"search_phrases":231,"slug":234,"view_count":166,"doi":235,"paper":236,"created_at":248},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":128,"substance":18,"depth":19,"authority":45,"freshness":192,"relevant":22,"comment":193},9,"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。",[195],{"name":123,"url":188},[27,28,197,198,199],"灌溉决策","安全保证","试验设计",{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[207],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":209},{"candidates":210,"agricultural_evidence":46},[211,216,222,227],{"code":201,"score":81,"supported":46,"title":212,"abstract":213,"tags":215},[202],[214,202],"灌溉",[197],{"code":32,"score":45,"supported":46,"title":217,"abstract":219,"tags":221},[218],"artificial intelligence",[220],"decision support",[28],{"code":52,"score":70,"supported":46,"title":223,"abstract":224,"tags":226},[],[56,225],"soil",[],{"code":59,"score":22,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[27],[232,233],"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":235,"openalex_id":237,"authors":238,"venue":123,"cited_by_count":166,"oa_url":188,"card":243,"direction":181,"ingested_from":183},"W7223131205",[239,241],{"name":240,"orcid":8},"Nassima Benkhelifa",{"name":242,"orcid":8},"Yacine Aït Ahmed",{"tldr":244,"method":245,"finding":246,"direction":181,"opportunity":247},"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":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":8,"source_name":123,"source_url":252,"published_at":124,"category":125,"cover_url":8,"hotness":13,"is_selected":14,"score":255,"score_detail":256,"sources":258,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":266,"search_phrases":281,"slug":284,"view_count":166,"doi":285,"paper":286,"created_at":300},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":128,"substance":18,"depth":19,"authority":45,"freshness":13,"relevant":22,"comment":257},"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。",[259],{"name":123,"url":252},[27,28,134,261,262],"作物病害识别","模型审计",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},[265],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":267},{"candidates":268,"agricultural_evidence":46},[269,273,277],{"code":138,"score":81,"supported":46,"title":270,"abstract":271,"tags":272},[150],[134,150],[134],{"code":32,"score":13,"supported":46,"title":274,"abstract":275,"tags":276},[155],[],[261,28],{"code":59,"score":22,"supported":14,"title":278,"abstract":279,"tags":280},[],[],[27],[282,283],"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":285,"openalex_id":287,"authors":288,"venue":123,"cited_by_count":166,"oa_url":252,"card":295,"direction":181,"ingested_from":183},"W7223088030",[289,291,293],{"name":290,"orcid":8},"Abdi-Basid Idriss Farah",{"name":292,"orcid":8},"Amina Abdillahi Guedi",{"name":294,"orcid":8},"Moussa Omar Youssouf",{"tldr":296,"method":297,"finding":298,"direction":181,"opportunity":299},"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":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":123,"source_url":304,"published_at":124,"category":125,"cover_url":8,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":311,"tags":313,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":336,"slug":339,"view_count":166,"doi":340,"paper":341,"created_at":351},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":128,"substance":19,"depth":309,"authority":192,"freshness":13,"relevant":22,"comment":310},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。",[312],{"name":123,"url":304},[27,28,314,315],"精准施药","病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":319},{"candidates":320,"agricultural_evidence":46},[321,325,331],{"code":32,"score":192,"supported":46,"title":322,"abstract":323,"tags":324},[218],[],[28],{"code":59,"score":21,"supported":46,"title":326,"abstract":327,"tags":330},[],[328,329],"精准农业","precision agriculture",[27],{"code":332,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},"F4",[],[],[314],[337,338],"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":340,"openalex_id":342,"authors":343,"venue":123,"cited_by_count":166,"oa_url":304,"card":346,"direction":181,"ingested_from":183},"W7223116087",[344],{"name":345,"orcid":8},"Achieng' Atim Okello",{"tldr":347,"method":348,"finding":349,"direction":181,"opportunity":350},"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",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":8,"source_name":123,"source_url":355,"published_at":124,"category":125,"cover_url":8,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":363,"primary_topic":367,"secondary_topics":368,"classification":369,"search_phrases":396,"slug":399,"view_count":166,"doi":400,"paper":401,"created_at":412},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。",74,{"impact":45,"substance":18,"depth":19,"authority":45,"freshness":13,"relevant":22,"comment":360},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[362],{"name":123,"url":355},[27,28,364,365,366],"粮食安全","遥感","气候分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":370},{"candidates":371,"agricultural_evidence":46},[372,376,382,387,391],{"code":32,"score":192,"supported":46,"title":373,"abstract":374,"tags":375},[218],[],[28],{"code":377,"score":53,"supported":46,"title":378,"abstract":379,"tags":381},"I5",[],[380],"农业传感",[],{"code":383,"score":22,"supported":14,"title":384,"abstract":385,"tags":386},"A8",[],[],[364],{"code":59,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],{"code":392,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},"I3",[],[],[365],[397,398],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":400,"openalex_id":402,"authors":403,"venue":123,"cited_by_count":166,"oa_url":355,"card":406,"direction":181,"ingested_from":183},"W7223111766",[404],{"name":405,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":407,"method":408,"finding":409,"direction":410,"opportunity":411},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","农业遥感与作物表型","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z"]