[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-992":3,"related-992":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":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},992,"黑龙江省农学会黑土地主要粮食作物及智慧农业科学传播专家团队受聘中国农学会 韩贵清出任首席科学传播专家","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-08\u002F26\u002Fcontent_570028.html","近日，中国农学会正式授予聘书，聘任黑龙江省农学会黑土地主要粮食作物及智慧农业科学传播专家团队为中国农学会科学传播专家团队。黑龙江省农业科学院二级研究员、国际黑土研究院主席韩贵清出任中国农学会首席科学传播专家。此次受聘的科学传播专家团队集聚省内农业领域一批优秀专业人才，围绕黑土地保护利用、主要粮食作物提质增产、智慧农业技术推广等关键领域，系统开展农业科学普及先进技术宣讲。韩贵清研究员表示将聚焦粮食安全与农业生态发展，打通农业科技落地『最后一公里』，把论文写在大地上、成果留在农民家。",null,"近日，中国农学会正式授予聘书，聘任黑龙江省农学会黑土地主要粮食作物及智慧农业科学传播专家团队为中国农学会科学传播专家团队。黑龙江省农业科学院二级研究员、国际黑土研究院主席韩贵清出任中国农学会首席科学传播专家。\n\n此次受聘的科学传播专家团队集聚省内农业领域一批优秀专业人才。团队聚焦黑土地保护利用、主要粮食作物提质增产、智慧农业技术推广等关键领域，依托专家资源优势，系统开展农业科学普及、先进技术宣讲、科研成果转化等系列工作。\n\n韩贵清研究员长期扎根东北农业一线，深耕黑土地保护修复、现代农业产业建设与农业科普传播工作，始终聚焦粮食安全与农业生态发展。他表示，团队在中国农学会的指导下，将立足东北黑土地保护利用、粮食安全和盐碱地改良，把科研成果和田间实践紧密结合，面向农业基层、广大农户做好科普宣讲与技术指导；打通农业科技落地“最后一公里”，普及黑土地保护、粮食作物栽培、智慧农业应用等实用知识技术，以科学传播赋能黑土地永续利用，真正把论文写在大地上，成果留在农民家。\n\n5700282026-08-26 16:18:05:749朱虹黑龙江专家团队受聘中国农学会科学传播专家团队1324滚动滚动","科技日报 2026-08-26 · 2026-08-26","2026-08-26T00:00:00Z","报道",10,false,63,{"impact":17,"substance":18,"depth":13,"authority":18,"freshness":19,"relevant":20,"comment":21},20,12,9,1,"黑龙江专家团队受聘中国农学会，聚焦黑土地保护与智慧农业科普，具省级影响力，内容实质但深度一般。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","黑龙江省农科院","韩贵清","国际黑土研究院","科学传播",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":39,"reason":62},[37,43,50,56],{"code":38,"score":18,"supported":39,"title":40,"abstract":41,"tags":42},"I1",true,[25],[25],[25],{"code":44,"score":45,"supported":39,"title":46,"abstract":47,"tags":49},"A8",3,[],[48],"粮食安全",[],{"code":51,"score":45,"supported":39,"title":52,"abstract":53,"tags":55},"H1",[],[54],"黑土地保护",[],{"code":57,"score":45,"supported":39,"title":58,"abstract":59,"tags":61},"H6",[],[60],"农业生态",[],"多个产业证据相当，需要确认主归属",[64,65],"国际黑土研究院 黑龙江省农科院 智慧农业 科学传播","国际黑土研究院 黑龙江省农科院","国际黑土研究院黑龙江省农科院智慧农业科学传播-992",0,"2026-08-27T00:05:19.049850Z",{"total":70,"page":20,"page_size":70,"items":71},6,[72,117,172,238,304,356],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":91,"secondary_topics":97,"classification":98,"search_phrases":111,"slug":114,"view_count":115,"doi":8,"paper":8,"created_at":116},623,"中国智慧农业第一镇暨'卫星+农业'项目在佳木斯启动","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529139.html","8-9,中国智慧农业第一镇暨'卫星+农业'项目启动会在佳木斯寰星航天科技有限公司举行,黑龙江省农业科学院副院长刘杰出席。该项目由市工信局、郊区政府、省农科院佳木斯分院、寰星航天联合发起,聚合哈尔滨工业大学航天学院、黑龙江省佳木斯生态环境监测中心等多家科研院所,落地郊区沿江镇,构建政产学研用金服协同创新格局,致力打造国家级智慧农业技术集成高地。","[![Image 2: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)![Image 3: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)](https:\u002F\u002Fh.mffb.com.cn\u002F)\n\n*    农业信息 \n    *   [农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")\n    *   [畜牧业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F65 \"畜牧业\")\n    *   [林业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F66 \"林业\")\n    *   [渔业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F67 \"渔业\")\n\n当前位置：[首页](https:\u002F\u002Fh.mffb.com.cn\u002F \"首页\")>[农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")>中国智慧农业第一镇暨“卫星+农业”项目启动 \n\n# [中国智慧农业第一镇暨“卫星+农业”项目启动 * 2026-08-17 07:52:39 中国智慧农业第一镇暨“卫星+农业”项目启动 ✦ 点击「上方文字」关注我们 ✦ 9日，中国智慧农业第一镇暨“卫星+农业”项目启动会在佳木斯寰星航天科技有限公司举行。市委常委、副市长李佶，黑龙江省农业科学院副院长刘杰出席会议并讲话。 据介绍，该项目由市工信局、郊区政府、省农科院佳木斯分院、寰星航天联合发起，汇聚哈尔滨工业大学航天学院、黑龙江省佳木斯生态环境监测中心等多家科研院所及相关领域重点企业，构建政、产、学、研、用、金、服协同创新格局。项目落地郊区沿江镇，依托其独特区位禀赋、优质黑土农业资源、完备基础设施及卫星产业配套优势，着力打造国家级智慧农业技术集成高地和现代农业示范观摩窗口，积极探索可复制、可推广的智慧农业发展佳木斯样板。 会议指出，该项目是践行农业现代化部署、培育农业新质生产力、推进乡村全面振兴的重要举措，依托卫星遥感、北斗定位、AI诊断、无人作业等前沿技术，赋能农业转型升级。各方要强化协同配合、突出科技赋能、做好示范引领，以经营主体带动全域普及，促进农业增效、农民增收，推动卫星产业与现代农业深度融合。 会上还举行了项目合作签约仪式。 记者 孙景卓 **END** **点击下方视频** **进入“今日佳木斯”视频号****了解更多内容** ![Image 4](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd4\u002F5609\u002F2026-05-11\u002F5609_17784904541011.webp) 来源：掌上佳木斯 编辑：赵丽娇 责编：李思语 审核：雷喜东 编审：张扬 ❄ ✦ ❄ ✦ ❄](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529139.html)\n\n 本文来自网友投稿或网络内容，如有侵犯您的权益请联系我们删除，联系邮箱：wyl860211@qq.com 。 \n\n### [便民服务进乡村---焉耆县2026年度农业机械、大型工程机械检验及驾驶员换证工作农机年检便民暖心落地](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529138.html \"便民服务进乡村---焉耆县2026年度农业机械、大型工程机械检验及驾驶员换证工作农机年检便民暖心落地\")\n\n### [转载 || 兰州大学草地农业科技学院:草学领域仅有的两位院士、我国草业科学领域目前唯一的草种创新与草地生态系统全国重点实验室](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529140.html \"转载 || 兰州大学草地农业科技学院:草学领域仅有的两位院士、我国草业科学领域目前唯一的草种创新与草地生态系统全国重点实验室\")\n\n### 最新文章\n\n*   [便民服务进乡村---焉耆县2026年度农业机械、大型工程机械检验及驾驶员换证工作农机年检便民暖心落地](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529138.html \"便民服务进乡村---焉耆县2026年度农业机械、大型工程机械检验及驾驶员换证工作农机年检便民暖心落地\")\n*   [农业增产技术 | 鲜食玉米低损高效机械化收获技术](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F529137.html \"农业增产技术 | 鲜食玉米低损高效机械化收获技术\")\n*   [科技赋能 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CONNECT:[ UseTime:0.001041s ] mysql:host=127.0.0.1;port=3306;dbname=h_mffb;charse","秸秆产业 2026-08-09 报道","2026-08-09T00:00:00Z",45,{"impact":82,"substance":18,"depth":83,"authority":70,"freshness":84,"relevant":20,"comment":85},15,8,4,"地方智慧农业项目启动，涉及卫星遥感等前沿技术，但报道较浅，信源为地方媒体，时效性一般。",[87],{"name":78,"url":75},[25,26,89,90],"卫星+农业","佳木斯",{"code":92,"slug":93,"name":94,"path":95,"parent_code":96},"G1","extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",[],{"status":99,"locked":14,"version":33,"method":34,"evidence":100},"classified",{"candidates":101,"agricultural_evidence":39},[102,106],{"code":38,"score":18,"supported":39,"title":103,"abstract":104,"tags":105},[25],[25],[25],{"code":92,"score":45,"supported":39,"title":107,"abstract":108,"tags":110},[],[109],"产学研",[],[112,113],"黑龙江省农科院 卫星+农业 智慧农业 佳木斯","黑龙江省农科院 卫星+农业","黑龙江省农科院卫星+农业智慧农业佳木斯-623",2,"2026-08-16T23:55:41.047581Z",{"id":118,"title":119,"url":120,"summary":121,"summary_zh":8,"content":122,"source_name":123,"source_url":8,"published_at":124,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":137,"secondary_topics":143,"classification":144,"search_phrases":167,"slug":170,"view_count":20,"doi":8,"paper":8,"created_at":171},311,"黑龙江省农科院联合中国农科院农业信息研究所发布「龙江农知大模型」——融合黑龙江省农业科研高质量文献与数据资源","https:\u002F\u002Fwww.dqdaily.com\u002F2026-08\u002F03\u002Fcontent_8150589.htm","2026年7月30日,人工智能赋能农业科技创新报告会暨\"龙江农知大模型\"发布会在哈尔滨举办。\"龙江农知大模型\"由黑龙江省农科院与中国农科院农业信息研究所共同研发,在中国农科院农业信息研究所2024年研发的\"农知大模型\"基础上,融合黑龙江省农业科研领域的高质量文献与专业科学数据资源,围绕农知增强检索、农技知识问答、农业数据分析、乡村振兴及智库服务方向,打造智能问答、智能写作、科研助手、数据分析、政策解读等智能化服务工具。","1.大庆网拥有大庆日报社所属媒体网上发布版权，任何单位及个人不得转载、摘编或以其它方式使用上述作品。已经授权使用作品的，应在授权范围内使用，并注明“来源：大庆网”。 2.凡本网注明“来源：XXX（非大庆网版权所有）”的作品，均转载自其它媒体，转载目的在于传递更多信息，并不代表本网赞同其观点和对其真实性负责。如其他媒体、网站或个人从本网下载使用，必须保留本网注明的“稿件来源”，并自负版权等法律责任。如擅自篡改为“稿件来源：大庆网”，大庆网将依法追究责任。","黑龙江省农业科学院\u002F大庆日报 2026-08-03","2026-08-03T00:00:00Z",58,{"impact":127,"substance":82,"depth":13,"authority":13,"freshness":128,"relevant":20,"comment":129},18,5,"省级农业科研机构发布农业大模型，具有行业进展价值，但正文为版权声明，实质信息有限。",[131],{"name":123,"url":120},[25,133,134,26,135,136],"农业大模型","中国农科院","农知大模型","龙江农知",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":99,"locked":14,"version":33,"method":34,"evidence":145},{"candidates":146,"agricultural_evidence":39},[147,152,157,163],{"code":138,"score":18,"supported":39,"title":148,"abstract":150,"tags":151},[149],"大模型",[149],[133],{"code":96,"score":45,"supported":39,"title":153,"abstract":154,"tags":156},[],[155],"乡村振兴",[],{"code":158,"score":45,"supported":39,"title":159,"abstract":160,"tags":162},"J1",[],[161],"政策解读",[],{"code":38,"score":20,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[25],[168,169],"黑龙江省农科院 中国农科院 农业大模型 农知大模型","黑龙江省农科院 中国农科院","黑龙江省农科院中国农科院农业大模型农知大模型-311","2026-08-08T23:53:55.647705Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":8,"source_name":178,"source_url":175,"published_at":179,"category":180,"cover_url":8,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":185,"tags":187,"primary_topic":192,"secondary_topics":197,"classification":199,"search_phrases":216,"slug":219,"view_count":67,"doi":220,"paper":221,"created_at":237},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":83,"substance":183,"depth":127,"authority":18,"freshness":13,"relevant":20,"comment":184},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。",[186],{"name":178,"url":175},[25,188,189,190,191],"农业人工智能","小农户","植物病害识别","可解释性",{"code":193,"slug":194,"name":195,"path":196,"parent_code":96},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services",[198],{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},{"status":99,"locked":14,"version":33,"method":34,"evidence":200},{"candidates":201,"agricultural_evidence":39},[202,207,212],{"code":193,"score":18,"supported":39,"title":203,"abstract":205,"tags":206},[204],"smallholder",[204],[189],{"code":138,"score":13,"supported":39,"title":208,"abstract":210,"tags":211},[209],"computer vision",[],[188,190],{"code":38,"score":20,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[25],[217,218],"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":220,"openalex_id":222,"authors":223,"venue":178,"cited_by_count":67,"oa_url":175,"card":230,"direction":234,"ingested_from":236},"W7223137323",[224,226,228],{"name":225,"orcid":8},"Akshay Ramgoolam",{"name":227,"orcid":8},"Priya Bundhoo Appadoo",{"name":229,"orcid":8},"Jean-Noël LEspérance",{"tldr":231,"method":232,"finding":233,"direction":234,"opportunity":235},"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":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":8,"source_name":178,"source_url":241,"published_at":179,"category":180,"cover_url":8,"hotness":13,"is_selected":14,"score":181,"score_detail":244,"sources":247,"tags":249,"primary_topic":253,"secondary_topics":259,"classification":261,"search_phrases":286,"slug":289,"view_count":67,"doi":290,"paper":291,"created_at":303},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":83,"substance":245,"depth":127,"authority":18,"freshness":19,"relevant":20,"comment":246},22,"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。",[248],{"name":178,"url":241},[25,188,250,251,252],"灌溉决策","安全保证","试验设计",{"code":254,"slug":255,"name":256,"path":257,"parent_code":258},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[260],{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},{"status":99,"locked":14,"version":33,"method":34,"evidence":262},{"candidates":263,"agricultural_evidence":39},[264,269,275,282],{"code":254,"score":82,"supported":39,"title":265,"abstract":266,"tags":268},[255],[267,255],"灌溉",[250],{"code":138,"score":18,"supported":39,"title":270,"abstract":272,"tags":274},[271],"artificial intelligence",[273],"decision support",[188],{"code":276,"score":70,"supported":39,"title":277,"abstract":278,"tags":281},"H2",[],[279,280],"土壤","soil",[],{"code":38,"score":20,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[25],[287,288],"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":290,"openalex_id":292,"authors":293,"venue":178,"cited_by_count":67,"oa_url":241,"card":298,"direction":234,"ingested_from":236},"W7223131205",[294,296],{"name":295,"orcid":8},"Nassima Benkhelifa",{"name":297,"orcid":8},"Yacine Aït Ahmed",{"tldr":299,"method":300,"finding":301,"direction":234,"opportunity":302},"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":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":8,"source_name":178,"source_url":307,"published_at":179,"category":180,"cover_url":8,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":336,"slug":339,"view_count":67,"doi":340,"paper":341,"created_at":355},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":83,"substance":245,"depth":127,"authority":18,"freshness":13,"relevant":20,"comment":312},"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。",[314],{"name":178,"url":307},[25,188,189,316,317],"作物病害识别","模型审计",{"code":193,"slug":194,"name":195,"path":196,"parent_code":96},[320],{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},{"status":99,"locked":14,"version":33,"method":34,"evidence":322},{"candidates":323,"agricultural_evidence":39},[324,328,332],{"code":193,"score":82,"supported":39,"title":325,"abstract":326,"tags":327},[204],[189,204],[189],{"code":138,"score":13,"supported":39,"title":329,"abstract":330,"tags":331},[209],[],[316,188],{"code":38,"score":20,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[25],[337,338],"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":340,"openalex_id":342,"authors":343,"venue":178,"cited_by_count":67,"oa_url":307,"card":350,"direction":234,"ingested_from":236},"W7223088030",[344,346,348],{"name":345,"orcid":8},"Abdi-Basid Idriss Farah",{"name":347,"orcid":8},"Amina Abdillahi Guedi",{"name":349,"orcid":8},"Moussa Omar Youssouf",{"tldr":351,"method":352,"finding":353,"direction":234,"opportunity":354},"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":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":8,"source_name":178,"source_url":359,"published_at":179,"category":180,"cover_url":8,"hotness":13,"is_selected":14,"score":362,"score_detail":363,"sources":366,"tags":368,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":392,"slug":395,"view_count":67,"doi":396,"paper":397,"created_at":407},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":83,"substance":127,"depth":364,"authority":19,"freshness":13,"relevant":20,"comment":365},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。",[367],{"name":178,"url":359},[25,188,369,370],"精准施药","病虫害监测",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},[],{"status":99,"locked":14,"version":33,"method":34,"evidence":374},{"candidates":375,"agricultural_evidence":39},[376,380,387],{"code":138,"score":19,"supported":39,"title":377,"abstract":378,"tags":379},[271],[],[188],{"code":38,"score":381,"supported":39,"title":382,"abstract":383,"tags":386},7,[],[384,385],"精准农业","precision agriculture",[25],{"code":388,"score":20,"supported":14,"title":389,"abstract":390,"tags":391},"F4",[],[],[369],[393,394],"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":396,"openalex_id":398,"authors":399,"venue":178,"cited_by_count":67,"oa_url":359,"card":402,"direction":234,"ingested_from":236},"W7223116087",[400],{"name":401,"orcid":8},"Achieng' Atim Okello",{"tldr":403,"method":404,"finding":405,"direction":234,"opportunity":406},"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"]