[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5066":3,"related-5066":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":72},5066,"When information is not enough: AI-enabled advisory and the fragile pathways of low-carbon transitions under system constraints","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105000","When information is not enough: AI-enabled advisory and the fragile pathways of low-carbon transitions under system constraints。Agricultural Systems",null,"Agricultural Systems","2026-10-16T00:00:00Z","论文",10,false,70,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},12,18,16,14,1,"核心期刊论文，探讨AI农业咨询在系统约束下推动低碳转型的局限，方法有新意但属学术探讨，公共影响有限。",[23],{"name":9,"url":6},[25,26,27,28],"农业人工智能","农业技术推广","低碳转型","农业系统",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"unclassified","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":51,"reason":52},[36,41,46],{"code":37,"score":20,"supported":13,"title":38,"abstract":39,"tags":40},"G1",[],[],[26],{"code":42,"score":20,"supported":13,"title":43,"abstract":44,"tags":45},"H4",[],[],[27],{"code":47,"score":20,"supported":13,"title":48,"abstract":49,"tags":50},"I2",[],[],[25],true,"缺少足够的主题证据",[54,55],"农业人工智能 农业技术推广 低碳转型 农业系统","农业人工智能 农业技术推广","农业人工智能农业技术推广低碳转型农业系统-5066",0,"10.1016\u002Fj.agsy.2026.105000",{"doi":58,"openalex_id":60,"authors":61,"venue":9,"cited_by_count":57,"oa_url":6,"card":8,"direction":8,"ingested_from":71},"W7222350379",[62,65,67,69],{"name":63,"orcid":64},"Dinh Duc Truong","https:\u002F\u002Forcid.org\u002F0000-0003-4125-7294",{"name":66,"orcid":8},"Bui Thi Hoang Lan",{"name":68,"orcid":8},"Ngo Thanh Mai",{"name":70,"orcid":8},"Huynh Thi Mai Dung","openalex","2026-10-16T23:30:04.129983Z",{"total":74,"page":20,"page_size":74,"items":75},6,[76,165,244,300,364,415],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":84,"score_detail":85,"sources":90,"tags":92,"primary_topic":96,"secondary_topics":102,"classification":108,"search_phrases":144,"slug":147,"view_count":74,"doi":148,"paper":149,"created_at":164},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",80,{"impact":17,"substance":86,"depth":17,"authority":87,"freshness":88,"relevant":20,"comment":89},22,13,9,"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[91],{"name":82,"url":79},[93,94,25,26,95],"数字乡村","智慧农业","小农户",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[103],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":109,"locked":13,"version":32,"method":33,"evidence":110},"classified",{"candidates":111,"agricultural_evidence":51},[112,118,127,135,139],{"code":97,"score":113,"supported":51,"title":114,"abstract":116,"tags":117},15,[115],"smallholder",[95,115],[95],{"code":47,"score":12,"supported":51,"title":119,"abstract":120,"tags":126},[],[121,122,123,124,125],"机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[25],{"code":128,"score":129,"supported":51,"title":130,"abstract":131,"tags":134},"I1",7,[],[132,133],"数字农业","digital agriculture",[94],{"code":37,"score":20,"supported":13,"title":136,"abstract":137,"tags":138},[],[],[26],{"code":140,"score":20,"supported":13,"title":141,"abstract":142,"tags":143},"I6",[],[],[93],[145,146],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":148,"openalex_id":150,"authors":151,"venue":82,"cited_by_count":57,"oa_url":79,"card":157,"direction":163,"ingested_from":71},"W7214205238",[152,154],{"name":153,"orcid":8},"Pravangkar Boruah",{"name":155,"orcid":156},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","农业人工智能与决策模型","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","数字乡村与农业信息化","2026-09-25T23:30:39.745514Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":8,"source_name":171,"source_url":168,"published_at":83,"category":11,"cover_url":8,"hotness":172,"is_selected":13,"score":173,"score_detail":174,"sources":178,"tags":183,"primary_topic":186,"secondary_topics":190,"classification":191,"search_phrases":217,"slug":220,"view_count":129,"doi":221,"paper":222,"created_at":243},3470,"AI-generated advice as a reinforcement layer in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.landusepol.2026.108336","Climate-smart agricultural (CSA) practices are central to food-system decarbonisation, yet adoption often falls short of farmers’ stated intentions, weakening the impact of incentives and extension under capacity constraints. We test whether spatially targeted, AI-generated advice can narrow this intention-action gap in a randomised field experiment with 1529 row crop farmers in Iowa, Illinois and Indiana during the cover crop decision window. Farmers assigned to receive four AI-generated emails were 4.45 %age points more likely to plant cover crops than controls (z = 2.81, p = 0.005), despite high baseline intentions in both groups. Effects operated on the extensive margin: there was no detectable change in the share of land planted among adopters. Survey responses indicate high engagement and a shift from untested optimism to more calibrated trust after exposure. Supervised AI advice can provide a low-cost, scalable complement to existing extension, improving follow-through and modestly expanding uptake without displacing human expertise.","气候智慧型农业（CSA）实践是食品系统脱碳的核心，然而在能力受限的情况下，农户的实际采用往往低于其声称的意愿，削弱了激励措施和推广服务的效果。我们在爱荷华州、伊利诺伊州和印第安纳州开展了一项随机田间试验，覆盖1529名大田作物种植户，在覆盖作物决策窗口期测试了空间靶向的AI生成建议能否缩小这一意愿—行动差距。被分配接收四封AI生成电子邮件的农户种植覆盖作物的概率比对照组高4.45个百分点（z = 2.81，p = 0.005），尽管两组基线意愿均较高。效应体现在广延边际上：采用者中种植土地比例未检测到显著变化。调查回复表明参与度较高，且在接触建议后，农户从未经检验的乐观转向更为校准的信任。有监督的AI建议可作为现有推广服务的低成本、可扩展补充，改善后续落实并适度扩大采用，而不会取代人类专业知识。","Land Use Policy",25,87,{"impact":86,"substance":175,"depth":176,"authority":19,"freshness":88,"relevant":20,"comment":177},23,19,"随机对照试验证实AI生成建议可低成本缩小农户意愿与行动差距，对智慧农业推广具参考价值。",[179,180],{"name":171,"url":168},{"name":181,"url":182},"Apollo","https:\u002F\u002Fdoi.org\u002F10.17863\u002Fcam.134741",[94,25,26,184,185],"覆盖作物","气候智慧农业",{"code":128,"slug":187,"name":188,"path":189,"parent_code":107},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":109,"locked":13,"version":32,"method":33,"evidence":192},{"candidates":193,"agricultural_evidence":51},[194,199,204,208,213],{"code":128,"score":88,"supported":51,"title":195,"abstract":197,"tags":198},[196],"smart agriculture",[],[94],{"code":200,"score":20,"supported":13,"title":201,"abstract":202,"tags":203},"A7",[],[],[184],{"code":37,"score":20,"supported":13,"title":205,"abstract":206,"tags":207},[],[],[26],{"code":209,"score":20,"supported":13,"title":210,"abstract":211,"tags":212},"H7",[],[],[185],{"code":47,"score":20,"supported":13,"title":214,"abstract":215,"tags":216},[],[],[25],[218,219],"AI生成建议 覆盖作物","爱荷华 伊利诺伊 印第安纳 覆盖作物","AI生成建议覆盖作物-3470","10.1016\u002Fj.landusepol.2026.108336",{"doi":221,"openalex_id":223,"authors":224,"venue":171,"cited_by_count":57,"oa_url":168,"card":237,"direction":242,"ingested_from":71},"W7214223143",[225,228,230,232,235],{"name":226,"orcid":227},"Callum Alexander","https:\u002F\u002Forcid.org\u002F0009-0007-5275-4583",{"name":229,"orcid":8},"Aiora Zabala",{"name":231,"orcid":8},"Andreas Kontoleon",{"name":233,"orcid":234},"Shalamar Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-1326-9936",{"name":236,"orcid":8},"Anuoluwa Sangotayo",{"tldr":238,"method":239,"finding":240,"direction":161,"opportunity":241},"随机试验检验AI生成建议能否缩小农户覆盖作物种植的意图-行动差距。","1529户美国中西部农户随机对照试验，四次AI生成邮件干预。","AI建议使覆盖作物种植率提高4.45个百分点，效果体现在是否采纳而非种植面积。","可探索AI建议与人工推广协同、长期持续效果及不同作物区域的异质性影响。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:09.887867Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":8,"source_name":250,"source_url":247,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":253,"sources":257,"tags":259,"primary_topic":262,"secondary_topics":263,"classification":265,"search_phrases":280,"slug":283,"view_count":57,"doi":284,"paper":285,"created_at":299},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",69,{"impact":254,"substance":255,"depth":17,"authority":16,"freshness":12,"relevant":20,"comment":256},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。",[258],{"name":250,"url":247},[94,25,95,260,261],"植物病害识别","可解释性",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},[264],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},{"status":109,"locked":13,"version":32,"method":33,"evidence":266},{"candidates":267,"agricultural_evidence":51},[268,272,276],{"code":97,"score":16,"supported":51,"title":269,"abstract":270,"tags":271},[115],[115],[95],{"code":47,"score":12,"supported":51,"title":273,"abstract":274,"tags":275},[125],[],[25,260],{"code":128,"score":20,"supported":13,"title":277,"abstract":278,"tags":279},[],[],[94],[281,282],"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":284,"openalex_id":286,"authors":287,"venue":250,"cited_by_count":57,"oa_url":247,"card":294,"direction":161,"ingested_from":71},"W7223137323",[288,290,292],{"name":289,"orcid":8},"Akshay Ramgoolam",{"name":291,"orcid":8},"Priya Bundhoo Appadoo",{"name":293,"orcid":8},"Jean-Noël LEspérance",{"tldr":295,"method":296,"finding":297,"direction":161,"opportunity":298},"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。","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。","2026-10-22T11:21:10.425290Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":250,"source_url":303,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":306,"sources":308,"tags":310,"primary_topic":314,"secondary_topics":320,"classification":322,"search_phrases":346,"slug":349,"view_count":57,"doi":350,"paper":351,"created_at":363},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":254,"substance":86,"depth":17,"authority":16,"freshness":88,"relevant":20,"comment":307},"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。",[309],{"name":250,"url":303},[94,25,311,312,313],"灌溉决策","安全保证","试验设计",{"code":315,"slug":316,"name":317,"path":318,"parent_code":319},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[321],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},{"status":109,"locked":13,"version":32,"method":33,"evidence":323},{"candidates":324,"agricultural_evidence":51},[325,330,335,342],{"code":315,"score":113,"supported":51,"title":326,"abstract":327,"tags":329},[316],[328,316],"灌溉",[311],{"code":47,"score":16,"supported":51,"title":331,"abstract":332,"tags":334},[124],[333],"decision support",[25],{"code":336,"score":74,"supported":51,"title":337,"abstract":338,"tags":341},"H2",[],[339,340],"土壤","soil",[],{"code":128,"score":20,"supported":13,"title":343,"abstract":344,"tags":345},[],[],[94],[347,348],"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":350,"openalex_id":352,"authors":353,"venue":250,"cited_by_count":57,"oa_url":303,"card":358,"direction":161,"ingested_from":71},"W7223131205",[354,356],{"name":355,"orcid":8},"Nassima Benkhelifa",{"name":357,"orcid":8},"Yacine Aït Ahmed",{"tldr":359,"method":360,"finding":361,"direction":161,"opportunity":362},"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":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":8,"source_name":250,"source_url":367,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":370,"sources":372,"tags":374,"primary_topic":377,"secondary_topics":378,"classification":380,"search_phrases":395,"slug":398,"view_count":57,"doi":399,"paper":400,"created_at":414},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":254,"substance":86,"depth":17,"authority":16,"freshness":12,"relevant":20,"comment":371},"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。",[373],{"name":250,"url":367},[94,25,95,375,376],"作物病害识别","模型审计",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},[379],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},{"status":109,"locked":13,"version":32,"method":33,"evidence":381},{"candidates":382,"agricultural_evidence":51},[383,387,391],{"code":97,"score":113,"supported":51,"title":384,"abstract":385,"tags":386},[115],[95,115],[95],{"code":47,"score":12,"supported":51,"title":388,"abstract":389,"tags":390},[125],[],[375,25],{"code":128,"score":20,"supported":13,"title":392,"abstract":393,"tags":394},[],[],[94],[396,397],"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":399,"openalex_id":401,"authors":402,"venue":250,"cited_by_count":57,"oa_url":367,"card":409,"direction":161,"ingested_from":71},"W7223088030",[403,405,407],{"name":404,"orcid":8},"Abdi-Basid Idriss Farah",{"name":406,"orcid":8},"Amina Abdillahi Guedi",{"name":408,"orcid":8},"Moussa Omar Youssouf",{"tldr":410,"method":411,"finding":412,"direction":161,"opportunity":413},"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":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":8,"source_name":250,"source_url":418,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":421,"score_detail":422,"sources":425,"tags":427,"primary_topic":430,"secondary_topics":431,"classification":432,"search_phrases":450,"slug":453,"view_count":57,"doi":454,"paper":455,"created_at":465},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":254,"substance":17,"depth":423,"authority":88,"freshness":12,"relevant":20,"comment":424},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。",[426],{"name":250,"url":418},[94,25,428,429],"精准施药","病虫害监测",{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},[],{"status":109,"locked":13,"version":32,"method":33,"evidence":433},{"candidates":434,"agricultural_evidence":51},[435,439,445],{"code":47,"score":88,"supported":51,"title":436,"abstract":437,"tags":438},[124],[],[25],{"code":128,"score":129,"supported":51,"title":440,"abstract":441,"tags":444},[],[442,443],"精准农业","precision agriculture",[94],{"code":446,"score":20,"supported":13,"title":447,"abstract":448,"tags":449},"F4",[],[],[428],[451,452],"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":454,"openalex_id":456,"authors":457,"venue":250,"cited_by_count":57,"oa_url":418,"card":460,"direction":161,"ingested_from":71},"W7223116087",[458],{"name":459,"orcid":8},"Achieng' Atim Okello",{"tldr":461,"method":462,"finding":463,"direction":161,"opportunity":464},"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"]