[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3467":3,"related-3467":120},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":119},3467,"Smart agriculture using digital holography and artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871510","In viticulture, direct protection against downy and powdery mildews relies on the preventive application of fungicides, requiring growers to anticipate infection events. Decision-making is mainly supported by forecasting models driven by weather predictions. However, these decisions are inherently uncertain, as some treatments ultimately prove unnecessary, although this information only becomes available retrospectively from observed conditions. This study explores the integration of an advanced spore detection device to better target fungicide applications in vineyards, aiming to reduce the use of phytosanitary products while maintaining high grape quality. The stand-alone device uses digital holography combined with artificial intelligence (AI) for detecting and classifying airborne spores of both downy and powdery mildew. It enables the tracking of disease dynamics as well as the assessment of environmental conditions and treatment effects on spore counts. Two case studies with real-time data access in Changins, Switzerland and Château le Puy, France, are presented and revealed promising strategies for substantial reductions in fungicide use while maintaining effective disease control.","在葡萄栽培中，对霜霉病和白粉病的直接防护依赖于杀菌剂的预防性施用，这要求种植者预判侵染事件的发生。决策主要依靠由天气预报驱动的预测模型来支持。然而，这些决策本质上具有不确定性，因为有些处理最终被证明是不必要的，尽管这一信息只能通过观测条件回顾性地获得。本研究探索了集成先进孢子检测装置以更精准地指导葡萄园杀菌剂施用的方法，旨在减少植物检疫产品的使用，同时保持葡萄的高品质。该独立装置利用数字全息术结合人工智能（AI）来检测和分类空气中的霜霉病和白粉病孢子。它能够追踪病害动态，并评估环境条件和处理措施对孢子数量的影响。本文介绍了在瑞士Changins和法国Château le Puy进行的两个可实时获取数据的案例研究，并揭示了在保持有效病害控制的同时大幅减少杀菌剂用量的有前景的策略。",null,"Frontiers in Plant Science","2026-09-24T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"数字全息结合AI检测葡萄病害孢子，为减少杀菌剂施用提供实证案例，方法新颖且具推广价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施药","葡萄种植","病害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,66],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},12,true,[48],"artificial intelligence",[48],[27],{"code":52,"score":53,"supported":46,"title":54,"abstract":56,"tags":57},"I1",9,[55],"smart agriculture",[],[26],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":65},"B2",7,[],[63,64],"葡萄","grape",[29],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"F4",[],[],[28],[72,73],"数字全息 孢子检测 葡萄","霜霉病 白粉病 人工智能","数字全息孢子检测葡萄-3467",4,"10.3389\u002Ffpls.2026.1871510",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":111,"oa_url":6,"card":112,"direction":116,"ingested_from":118},"W7214152870",[80,82,84,87,90,93,96,98,100,103,106,109],{"name":81,"orcid":9},"Tessa Chiara Basso",{"name":83,"orcid":9},"Sara Leoni",{"name":85,"orcid":86},"Denis Ullmann","https:\u002F\u002Forcid.org\u002F0000-0002-7179-005X",{"name":88,"orcid":89},"Adimulya Kartiyasa","https:\u002F\u002Forcid.org\u002F0009-0003-3142-1174",{"name":91,"orcid":92},"Livio Ruzzante","https:\u002F\u002Forcid.org\u002F0000-0002-8693-8678",{"name":94,"orcid":95},"Sylvain Schnée","https:\u002F\u002Forcid.org\u002F0000-0002-1014-1961",{"name":97,"orcid":9},"Anne‐Lise Fabre",{"name":99,"orcid":9},"Steven Hewison",{"name":101,"orcid":102},"Jérôme Kasparian","https:\u002F\u002Forcid.org\u002F0000-0003-2398-3882",{"name":104,"orcid":105},"Nicolas Berti","https:\u002F\u002Forcid.org\u002F0000-0003-3769-3966",{"name":107,"orcid":108},"Pierre‐Henri Dubuis","https:\u002F\u002Forcid.org\u002F0000-0002-9624-3925",{"name":110,"orcid":9},"Jean-Pierre Wolf",0,{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"利用数字全息与AI检测葡萄园空气中霜霉和白粉病菌孢子，优化杀菌剂施用。","数字全息成像结合AI分类孢子，在瑞士和法国葡萄园实时监测。","孢子检测可追踪病害动态，在保持防效下大幅减少杀菌剂使用。","智慧农业 \u002F 农业物联网","可探索孢子自动监测与气象预报融合的精准施药决策模型，并验证多作物适用性。","openalex","2026-09-25T23:30:09.459179Z",{"total":121,"page":21,"page_size":121,"items":122},6,[123,175,258,314,374,438],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":129,"source_url":126,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":135,"tags":137,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":158,"slug":161,"view_count":111,"doi":162,"paper":163,"created_at":174},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",62,{"impact":20,"substance":17,"depth":133,"authority":53,"freshness":13,"relevant":21,"comment":134},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。",[136],{"name":129,"url":126},[26,27,28,138],"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":142},{"candidates":143,"agricultural_evidence":46},[144,148,154],{"code":32,"score":53,"supported":46,"title":145,"abstract":146,"tags":147},[48],[],[27],{"code":52,"score":60,"supported":46,"title":149,"abstract":150,"tags":153},[],[151,152],"精准农业","precision agriculture",[26],{"code":67,"score":21,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[28],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":129,"cited_by_count":111,"oa_url":126,"card":168,"direction":172,"ingested_from":118},"W7223116087",[166],{"name":167,"orcid":9},"Achieng' Atim Okello",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"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。","农业人工智能与决策模型","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":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":199,"classification":206,"search_phrases":231,"slug":234,"view_count":21,"doi":235,"paper":236,"created_at":257},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":185,"substance":186,"depth":17,"authority":187,"freshness":53,"relevant":21,"comment":188},16,21,13,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[190],{"name":181,"url":178},[26,192,27,193,30],"无人机","遥感监测",{"code":59,"slug":195,"name":196,"path":197,"parent_code":198},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[200,201],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":202,"slug":203,"name":204,"path":205,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,215,222,227],{"code":59,"score":133,"supported":46,"title":210,"abstract":212,"tags":214},[211],"apple",[213,63,211],"苹果",[],{"code":32,"score":216,"supported":46,"title":217,"abstract":219,"tags":221},15,[218],"deep learning",[220,218],"深度学习",[27],{"code":202,"score":45,"supported":46,"title":223,"abstract":225,"tags":226},[224],"multispectral",[224],[193],{"code":52,"score":60,"supported":46,"title":228,"abstract":229,"tags":230},[],[151,152],[26],[232,233],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":235,"openalex_id":237,"authors":238,"venue":181,"cited_by_count":111,"oa_url":250,"card":251,"direction":116,"ingested_from":118},"W7221558726",[239,242,245,247],{"name":240,"orcid":241},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":243,"orcid":244},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":246,"orcid":9},"Fatih Sinan Bir",{"name":248,"orcid":249},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":252,"method":253,"finding":254,"direction":255,"opportunity":256},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":9,"content":9,"source_name":263,"source_url":261,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":290,"slug":293,"view_count":294,"doi":295,"paper":296,"created_at":313},2620,"A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10444-4","A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation。Precision Agriculture","Precision Agriculture","2026-09-16T00:00:00Z",70,{"impact":45,"substance":17,"depth":185,"authority":19,"freshness":13,"relevant":21,"comment":267},"核心期刊论文，方法新颖且时效性强，但属细分作物技术进展，产业影响范围有限。",[269],{"name":263,"url":261},[26,27,28,271,272],"机器视觉","大白菜",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":46},[278,282,286],{"code":32,"score":187,"supported":46,"title":279,"abstract":280,"tags":281},[218],[218],[27,271],{"code":52,"score":75,"supported":46,"title":283,"abstract":284,"tags":285},[],[152],[26],{"code":67,"score":21,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[28],[291,292],"农业人工智能 智慧农业 机器视觉 精准施药","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准施药-2620",2,"10.1007\u002Fs11119-026-10444-4",{"doi":295,"openalex_id":297,"authors":298,"venue":263,"cited_by_count":111,"oa_url":9,"card":9,"direction":9,"ingested_from":118},"W7213275445",[299,301,303,305,308,311],{"name":300,"orcid":9},"Changxi Liu",{"name":302,"orcid":9},"Hang Shi",{"name":304,"orcid":9},"Hao Sun",{"name":306,"orcid":307},"Hui Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8843-7298",{"name":309,"orcid":310},"Qingda Li","https:\u002F\u002Forcid.org\u002F0000-0002-9046-2094",{"name":312,"orcid":9},"Jun Hu","2026-09-16T23:30:03.326664Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":129,"source_url":317,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":323,"tags":325,"primary_topic":329,"secondary_topics":335,"classification":337,"search_phrases":354,"slug":357,"view_count":111,"doi":358,"paper":359,"created_at":373},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。",69,{"impact":20,"substance":186,"depth":17,"authority":45,"freshness":13,"relevant":21,"comment":322},"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。",[324],{"name":129,"url":317},[26,27,326,327,328],"小农户","植物病害识别","可解释性",{"code":330,"slug":331,"name":332,"path":333,"parent_code":334},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[336],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":338},{"candidates":339,"agricultural_evidence":46},[340,345,350],{"code":330,"score":45,"supported":46,"title":341,"abstract":343,"tags":344},[342],"smallholder",[342],[326],{"code":32,"score":13,"supported":46,"title":346,"abstract":348,"tags":349},[347],"computer vision",[],[27,327],{"code":52,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[26],[355,356],"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":358,"openalex_id":360,"authors":361,"venue":129,"cited_by_count":111,"oa_url":317,"card":368,"direction":172,"ingested_from":118},"W7223137323",[362,364,366],{"name":363,"orcid":9},"Akshay Ramgoolam",{"name":365,"orcid":9},"Priya Bundhoo Appadoo",{"name":367,"orcid":9},"Jean-Noël LEspérance",{"tldr":369,"method":370,"finding":371,"direction":172,"opportunity":372},"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":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":9,"source_name":129,"source_url":377,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":380,"sources":382,"tags":384,"primary_topic":388,"secondary_topics":394,"classification":396,"search_phrases":420,"slug":423,"view_count":111,"doi":424,"paper":425,"created_at":437},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":20,"substance":18,"depth":17,"authority":45,"freshness":53,"relevant":21,"comment":381},"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。",[383],{"name":129,"url":377},[26,27,385,386,387],"灌溉决策","安全保证","试验设计",{"code":389,"slug":390,"name":391,"path":392,"parent_code":393},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[395],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":46},[399,404,409,416],{"code":389,"score":216,"supported":46,"title":400,"abstract":401,"tags":403},[390],[402,390],"灌溉",[385],{"code":32,"score":45,"supported":46,"title":405,"abstract":406,"tags":408},[48],[407],"decision support",[27],{"code":410,"score":121,"supported":46,"title":411,"abstract":412,"tags":415},"H2",[],[413,414],"土壤","soil",[],{"code":52,"score":21,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[26],[421,422],"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":424,"openalex_id":426,"authors":427,"venue":129,"cited_by_count":111,"oa_url":377,"card":432,"direction":172,"ingested_from":118},"W7223131205",[428,430],{"name":429,"orcid":9},"Nassima Benkhelifa",{"name":431,"orcid":9},"Yacine Aït Ahmed",{"tldr":433,"method":434,"finding":435,"direction":172,"opportunity":436},"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":439,"title":440,"url":441,"summary":442,"summary_zh":443,"content":9,"source_name":129,"source_url":441,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":454,"search_phrases":469,"slug":472,"view_count":111,"doi":473,"paper":474,"created_at":488},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":20,"substance":18,"depth":17,"authority":45,"freshness":13,"relevant":21,"comment":445},"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。",[447],{"name":129,"url":441},[26,27,326,449,450],"作物病害识别","模型审计",{"code":330,"slug":331,"name":332,"path":333,"parent_code":334},[453],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":455},{"candidates":456,"agricultural_evidence":46},[457,461,465],{"code":330,"score":216,"supported":46,"title":458,"abstract":459,"tags":460},[342],[326,342],[326],{"code":32,"score":13,"supported":46,"title":462,"abstract":463,"tags":464},[347],[],[449,27],{"code":52,"score":21,"supported":14,"title":466,"abstract":467,"tags":468},[],[],[26],[470,471],"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":473,"openalex_id":475,"authors":476,"venue":129,"cited_by_count":111,"oa_url":441,"card":483,"direction":172,"ingested_from":118},"W7223088030",[477,479,481],{"name":478,"orcid":9},"Abdi-Basid Idriss Farah",{"name":480,"orcid":9},"Amina Abdillahi Guedi",{"name":482,"orcid":9},"Moussa Omar Youssouf",{"tldr":484,"method":485,"finding":486,"direction":172,"opportunity":487},"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"]