[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2124":3,"related-2124":101},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":100},2124,"Cysteine quantification in pea cultivars from SERS spectra using AI","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102557","Rapid quantification of sulfur-containing amino acids, particularly cysteine, in legumes is critical for assessing nutritional quality, supporting breeding program screening, and ensuring consistency in quality control processes. However, conventional methods, such as high-performance liquid chromatography (HPLC), are time-consuming and resource-intensive for high-throughput applications. This study evaluated artificial intelligence models for predicting cultivar-level mean cysteine concentration from surface-enhanced Raman spectroscopy (SERS) spectra of pea extracts. SERS spectra were acquired from 20 cultivars grown at three field locations, with HPLC measurements across the three locations averaged to provide a cultivar-level mean cysteine reference value. Linear regression, partial least squares regression, support vector regression, random forest regression, and a one-dimensional convolutional neural network (1D-CNN) were compared using within-cultivar splits and leave-one-cultivar-out (LOCO) evaluation. The 1D-CNN achieved good predictive performance for the represented cultivars, with an RMSE of 0.005 g\u002F100 g and an MAE of 0.004 g\u002F100 g. Under LOCO evaluation, RMSE and MAE increased to 0.013 and 0.012 g\u002F100 g, respectively. These results provide proof of concept for SERS\u002FAI prediction of multi-environment, cultivar-level mean cysteine concentration, with LOCO providing encouraging evidence of transferability to unseen cultivars within the represented environments and analytical conditions. Shapley Additive Explanations analysis identified contributions from both substrate-related and molecular spectral features. Broader validation using independent samples, locations, extractions, substrates, and reference measurements is required to establish general analytical applicability. A simulated additive-noise sensitivity analysis further evaluated model performance under increasing spectral noise.","豆类中含硫氨基酸，尤其是半胱氨酸的快速定量，对于评估营养品质、支持育种项目筛选以及确保质量控制流程的一致性至关重要。然而，传统方法如高效液相色谱法（HPLC）在高通量应用中耗时且资源密集。本研究评估了人工智能模型从豌豆提取物的表面增强拉曼光谱（SERS）中预测品种水平平均半胱氨酸浓度的能力。SERS光谱采集自三个田间地点种植的20个品种，并将三个地点的HPLC测量值取平均以提供品种水平的平均半胱氨酸参考值。采用品种内划分和留一品种法（LOCO）评估，比较了线性回归、偏最小二乘回归、支持向量回归、随机森林回归和一维卷积神经网络（1D-CNN）。1D-CNN对所代表品种取得了良好的预测性能，RMSE为0.005 g\u002F100 g，MAE为0.004 g\u002F100 g。在LOCO评估下，RMSE和MAE分别增至0.013和0.012 g\u002F100 g。这些结果为SERS\u002F人工智能预测多环境、品种水平平均半胱氨酸浓度提供了概念验证，LOCO为在已代表的环境和分析条件下向未见品种的可迁移性提供了令人鼓舞的证据。Shapley加性解释分析识别了底物相关和分子光谱特征的贡献。需要使用独立样本、地点、提取方法、底物和参考测量进行更广泛的验证，以确立普遍的分析适用性。模拟加性噪声敏感性分析进一步评估了模型在光谱噪声增加条件下的性能。",null,"Smart Agricultural Technology","2026-09-08T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,13,6,1,"SERS光谱结合AI实现豌豆半胱氨酸快速定量，为育种筛选与品质控制提供概念验证，方法新颖但尚需更广泛验证。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","品质育种","豌豆","光谱检测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":62},[39,47,52,57],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I2",4,true,[],[45],"artificial intelligence",[28],{"code":48,"score":22,"supported":14,"title":49,"abstract":50,"tags":51},"A5",[],[],[30],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"F2",[],[],[29],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[27],"农业相关性需要复核",[64,65],"农业人工智能 光谱检测 品质育种 智慧农业","农业人工智能 光谱检测","农业人工智能光谱检测品质育种智慧农业-2124",2,"10.1016\u002Fj.atech.2026.102557",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":91,"oa_url":92,"card":93,"direction":97,"ingested_from":99},"W7211935637",[72,75,78,81,84,86,88],{"name":73,"orcid":74},"Elham Gorgannejad","https:\u002F\u002Forcid.org\u002F0009-0001-6375-0429",{"name":76,"orcid":77},"Qian Liu","https:\u002F\u002Forcid.org\u002F0000-0001-9832-596X",{"name":79,"orcid":80},"Catherine Rui Jin Findlay","https:\u002F\u002Forcid.org\u002F0000-0002-9304-1033",{"name":82,"orcid":83},"Mohammad Nadimi","https:\u002F\u002Forcid.org\u002F0000-0002-4550-7572",{"name":85,"orcid":9},"Alex Chun-Te Ko",{"name":87,"orcid":9},"Pankaj Bhowmik",{"name":89,"orcid":90},"Jitendra Paliwal","https:\u002F\u002Forcid.org\u002F0000-0002-1665-3626",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007823\u002Fpdf",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"用SERS光谱结合AI预测豌豆品种半胱氨酸含量，1D-CNN表现最佳。","SERS光谱、HPLC参考值、多种回归模型及1D-CNN，采用LOCO评估。","1D-CNN在品种内预测RMSE为0.005 g\u002F100g，LOCO下为0.013 g\u002F100g，具","农业人工智能与决策模型","可扩展至其他氨基酸或作物，结合便携SERS与迁移学习实现田间高通量品质筛查。","openalex","2026-09-11T23:30:03.956325Z",{"total":21,"page":22,"page_size":21,"items":102},[103,169,223,267,329,393],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":118,"tags":120,"primary_topic":123,"secondary_topics":128,"classification":129,"search_phrases":151,"slug":154,"view_count":155,"doi":156,"paper":157,"created_at":168},4907,"PEAS-DeiT: Multi-Scale Spatial Attention Enhanced DeiT for Pea Leaf Disease Classification","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp162010113","Early and accurate detection of pea diseases is essential for reducing crop losses and supporting precision agriculture. Although deep learning methods have shown good performance in plant disease classification, CNN-based models may have limitations in learning long-range relationships and detailed disease features. In this study, an explainable transformer-based framework, named PEAS-DeiT, is proposed for automated pea leaf disease classification. The proposed approach integrates a multi-scale spatial attention mechanism into the Data-efficient Image Transformer (DeiT) architecture to enhance the representation of disease-related visual features. A unified pea leaf disease dataset (UPLDD) was constructed by combining multiple publicly available datasets, covering six classes, including five disease categories and healthy samples. The proposed model was evaluated against CNN-based and transformer-based architectures using five-fold cross-validation. Furthermore, explainable artificial intelligence (XAI) analysis was performed to investigate the regions contributing to model predictions. Experimental results demonstrate that PEAS-DeiT achieved an accuracy of 93.11%, showing competitive performance compared with the evaluated CNN and transformer-based models. The gradient-based saliency analysis also revealed localized response patterns in several evaluated samples, providing qualitative insight into the image regions contributing to model predictions. Statistical analyses were conducted to evaluate the performance differences among the compared models. These findings indicate that PEAS-DeiT provides a competitive approach for pea leaf disease classification within the evaluated experimental setting.","豌豆病害的早期准确检测对于减少作物损失和支持精准农业至关重要。尽管深度学习方法在植物病害分类中表现出良好性能，但基于卷积神经网络（CNN）的模型在学习长程关系和病害细节特征方面可能存在局限。本研究提出了一种基于Transformer的可解释框架PEAS-DeiT，用于豌豆叶片病害的自动化分类。该方法将多尺度空间注意力机制集成到数据高效图像Transformer（DeiT）架构中，以增强病害相关视觉特征的表征能力。通过整合多个公开数据集，构建了统一的豌豆叶片病害数据集（UPLDD），涵盖六个类别，包括五类病害和健康样本。采用五折交叉验证，将所提模型与基于CNN和基于Transformer的架构进行了评估比较。此外，进行了可解释人工智能（XAI）分析，以探究对模型预测有贡献的区域。实验结果表明，PEAS-DeiT达到了93.11%的准确率，与所评估的基于CNN和基于Transformer的模型相比展现出有竞争力的性能。基于梯度的显著性分析还揭示了若干评估样本中的局部响应模式，为理解对模型预测有贡献的图像区域提供了定性见解。通过统计分析评估了所比较模型之间的性能差异。这些发现表明，在所评估的实验设置下，PEAS-DeiT为豌豆叶片病害分类提供了一种有竞争力的方法。","Applied Sciences","2026-10-13T00:00:00Z",64,{"impact":113,"substance":114,"depth":115,"authority":20,"freshness":116,"relevant":22,"comment":117},8,18,16,9,"提出多尺度空间注意力增强的DeiT模型用于豌豆叶病害分类，构建六类统一数据集并达93.11%准确率，方法有创新但属实验室验证阶段，产业影响有限。",[119],{"name":109,"url":106},[27,28,121,122,30],"可解释AI","植物病害识别",{"code":40,"slug":124,"name":125,"path":126,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":130,"locked":14,"version":35,"method":36,"evidence":131},"classified",{"candidates":132,"agricultural_evidence":42},[133,140,147],{"code":40,"score":134,"supported":42,"title":135,"abstract":136,"tags":139},12,[],[137,138,45],"深度学习","deep learning",[28,121,122],{"code":58,"score":141,"supported":42,"title":142,"abstract":143,"tags":146},7,[],[144,145],"精准农业","precision agriculture",[27],{"code":48,"score":22,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[30],[152,153],"PEAS-DeiT 豌豆叶病害","DeiT 植物病害分类","PEAS-DeiT豌豆叶病害-4907",3,"10.3390\u002Fapp162010113",{"doi":156,"openalex_id":158,"authors":159,"venue":109,"cited_by_count":91,"oa_url":106,"card":163,"direction":97,"ingested_from":99},"W7221612639",[160],{"name":161,"orcid":162},"Fatma Ozge Ozkok","https:\u002F\u002Forcid.org\u002F0000-0002-1421-4670",{"tldr":164,"method":165,"finding":166,"direction":97,"opportunity":167},"提出PEAS-DeiT模型，将多尺度空间注意力融入DeiT，实现豌豆叶片病害分类。","多尺度空间注意力增强DeiT，构建UPLDD数据集，五折交叉验证与XAI分析。","PEAS-DeiT达到93.11%准确率，优于对比的CNN和Transformer模型。","可探索轻量化Transformer在移动端病害识别中的部署，并扩展多作物跨域泛化能力。","2026-10-14T23:30:46.091813Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":9,"content":9,"source_name":174,"source_url":9,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":183,"primary_topic":186,"secondary_topics":192,"classification":193,"search_phrases":208,"slug":211,"view_count":212,"doi":9,"paper":213,"created_at":222},3323,"基于空间分辨光谱的鲜食玉米含水率和硬度MaMoNet检测模型——融合Mamba状态空间模型与多门专家混合机制","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5953466437_162dab0450670bdama.html","《智慧农业（中英文）》2026年第4期。许敏、赵鑫、陈艳萍、朱启兵、黄敏（江南大学\u002F江苏省农科院）以带苞叶鲜食玉米为研究对象，构建多通道可见光-近红外空间分辨光谱采集系统，获取玉米样本的多通道光谱数据，并提出一种融合Mamba状态空间模型与多门专家混合(MMoE)机制的多任务预测网络——MaMoNet(Mamba-MMoE Network)。MaMoNet在测试集上玉米籽粒含水率预测的决定系数R²达到了0.91，硬度预测R²达到了0.89，均优于对比模型。消融实验进一步证明Mamba模块在建模光谱长程依赖关系方面具有显著优势，以及MMoE机制能够有效缓解多任务学习中任务间特征竞争问题。","《智慧农业（中英文）》2026年第4期","2026-09-20T00:00:00Z",81,{"impact":114,"substance":178,"depth":114,"authority":179,"freshness":116,"relevant":22,"comment":180},22,14,"核心期刊论文，方法新颖且指标可靠，对农产品无损检测有实质参考价值。",[182],{"name":174,"url":172},[27,28,184,31,185],"鲜食玉米","多任务学习",{"code":187,"slug":188,"name":189,"path":190,"parent_code":191},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":130,"locked":14,"version":35,"method":36,"evidence":194},{"candidates":195,"agricultural_evidence":42},[196,200,204],{"code":187,"score":134,"supported":42,"title":197,"abstract":198,"tags":199},[189],[189],[184],{"code":58,"score":41,"supported":42,"title":201,"abstract":202,"tags":203},[],[27],[27],{"code":40,"score":22,"supported":14,"title":205,"abstract":206,"tags":207},[],[],[28],[209,210],"江南大学 鲜食玉米 含水率","MaMoNet 玉米 硬度","江南大学鲜食玉米含水率-3323",5,{"doi":9,"openalex_id":9,"authors":214,"venue":9,"cited_by_count":91,"oa_url":9,"card":215,"direction":219,"ingested_from":221},[],{"tldr":216,"method":217,"finding":218,"direction":219,"opportunity":220},"提出MaMoNet网络，用空间分辨光谱检测鲜食玉米含水率和硬度。","多通道可见光-近红外空间分辨光谱，融合Mamba与MMoE多任务网络。","含水率R²达0.91、硬度R²达0.89，优于对比模型，Mamba与MMoE均有效。","农业遥感与作物表型","可探索Mamba在多作物多品质指标无损检测中的泛化性及田间在线部署。","agent","2026-09-24T00:04:02.557420Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":9,"content":228,"source_name":229,"source_url":9,"published_at":230,"category":231,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":232,"sources":234,"tags":236,"primary_topic":238,"secondary_topics":239,"classification":240,"search_phrases":262,"slug":265,"view_count":155,"doi":9,"paper":9,"created_at":266},2084,"中国农科院资划所基于可解释AI构建农产品产地溯源模型 破解光谱判断产地的\"黑箱\"难题","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F01c47c41992241608017a544cbad3538.htm","中国农科院农业资源与农业区划研究所智慧农业创新团队基于可解释人工智能构建农产品产地溯源模型,以中国六个地区的猕猴桃为对象,融合光谱成像与多模型组合策略,并运用可解释性方法对最优组合模型进行分层和整体解读,在提升溯源准确率的同时揭示光谱数据判断产地的内在逻辑,为光谱技术在农业生产与品质监管中应用提供系统解决思路,成果发表于《农业人工智能》(Artificial Intelligence in Agriculture)。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fwww.caas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 2](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 3](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 4](https:\u002F\u002Fwww.caas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fwww.caas.cn\u002Findex.htm)\n\n*   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019.jpg](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2026-09\u002Ffa69a734a4bc4551a1ea07fc7f019a58.jpg)\n\n利用光谱数据判断农产品产地时，现有方法多依赖单一算法，往往只追求预测准确度，却忽略了模型决策逻辑的解释，限制了可靠溯源模型的开发和对判断过程的理解。科研团队以中国六个地区的猕猴桃为对象，融合光谱成像技术与多模型组合策略构建产地溯源模型，并运用可解释性分析方法对最优组合模型进行分层和整体解读，在提升溯源准确率的同时，揭示了光谱数据背后判断产地的内在逻辑。\n\n该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路，在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值，有助于推动光谱技术在农业生产与品质监管中的实际应用。\n\n该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。（通讯员 姬悦）\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [黑土地侵蚀沟时空演变规律获揭示](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F1b6aad7e04244664a16d4fb7847890d7.htm)2026-09-07  \n*   [时代·人民·文明——中国元首外交的思想启迪](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fszyw\u002F1c34df72522047cda6b6ee907c331b85.htm)2026-09-06  \n*   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京公网安备11010802048565号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802048565)","中国农业科学院","2026-09-09T00:00:00Z","报道",{"impact":178,"substance":18,"depth":19,"authority":179,"freshness":113,"relevant":22,"comment":233},"国家级科研机构将可解释AI引入农产品产地溯源，方法新颖、结论可靠，对智慧农业质量监管有示范价值。",[235],{"name":229,"url":226},[27,28,121,237,31],"农产品溯源",{"code":40,"slug":124,"name":125,"path":126,"parent_code":127},[],{"status":130,"locked":14,"version":35,"method":36,"evidence":241},{"candidates":242,"agricultural_evidence":42},[243,247,251,257],{"code":40,"score":113,"supported":42,"title":244,"abstract":245,"tags":246},[],[28,45],[28,121],{"code":58,"score":41,"supported":42,"title":248,"abstract":249,"tags":250},[],[27],[27],{"code":252,"score":155,"supported":42,"title":253,"abstract":254,"tags":256},"B2",[],[255],"猕猴桃",[],{"code":258,"score":67,"supported":14,"title":259,"abstract":260,"tags":261},"E4",[],[],[31,237],[263,264],"农业人工智能 农产品溯源 光谱检测 智慧农业","农业人工智能 农产品溯源","农业人工智能农产品溯源光谱检测智慧农业-2084","2026-09-11T00:04:19.946470Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":279,"tags":281,"primary_topic":284,"secondary_topics":290,"classification":292,"search_phrases":309,"slug":312,"view_count":91,"doi":313,"paper":314,"created_at":328},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":113,"substance":277,"depth":114,"authority":134,"freshness":13,"relevant":22,"comment":278},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。",[280],{"name":273,"url":270},[27,28,282,122,283],"小农户","可解释性",{"code":285,"slug":286,"name":287,"path":288,"parent_code":289},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[291],{"code":40,"slug":124,"name":125,"path":126,"parent_code":127},{"status":130,"locked":14,"version":35,"method":36,"evidence":293},{"candidates":294,"agricultural_evidence":42},[295,300,305],{"code":285,"score":134,"supported":42,"title":296,"abstract":298,"tags":299},[297],"smallholder",[297],[282],{"code":40,"score":13,"supported":42,"title":301,"abstract":303,"tags":304},[302],"computer vision",[],[28,122],{"code":58,"score":22,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[27],[310,311],"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":313,"openalex_id":315,"authors":316,"venue":273,"cited_by_count":91,"oa_url":270,"card":323,"direction":97,"ingested_from":99},"W7223137323",[317,319,321],{"name":318,"orcid":9},"Akshay Ramgoolam",{"name":320,"orcid":9},"Priya Bundhoo Appadoo",{"name":322,"orcid":9},"Jean-Noël LEspérance",{"tldr":324,"method":325,"finding":326,"direction":97,"opportunity":327},"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":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":9,"source_name":273,"source_url":332,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":335,"sources":337,"tags":339,"primary_topic":343,"secondary_topics":349,"classification":351,"search_phrases":375,"slug":378,"view_count":91,"doi":379,"paper":380,"created_at":392},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":113,"substance":178,"depth":114,"authority":134,"freshness":116,"relevant":22,"comment":336},"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。",[338],{"name":273,"url":332},[27,28,340,341,342],"灌溉决策","安全保证","试验设计",{"code":344,"slug":345,"name":346,"path":347,"parent_code":348},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[350],{"code":40,"slug":124,"name":125,"path":126,"parent_code":127},{"status":130,"locked":14,"version":35,"method":36,"evidence":352},{"candidates":353,"agricultural_evidence":42},[354,359,364,371],{"code":344,"score":17,"supported":42,"title":355,"abstract":356,"tags":358},[345],[357,345],"灌溉",[340],{"code":40,"score":134,"supported":42,"title":360,"abstract":361,"tags":363},[45],[362],"decision support",[28],{"code":365,"score":21,"supported":42,"title":366,"abstract":367,"tags":370},"H2",[],[368,369],"土壤","soil",[],{"code":58,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[27],[376,377],"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":379,"openalex_id":381,"authors":382,"venue":273,"cited_by_count":91,"oa_url":332,"card":387,"direction":97,"ingested_from":99},"W7223131205",[383,385],{"name":384,"orcid":9},"Nassima Benkhelifa",{"name":386,"orcid":9},"Yacine Aït Ahmed",{"tldr":388,"method":389,"finding":390,"direction":97,"opportunity":391},"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":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":273,"source_url":396,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":425,"slug":428,"view_count":91,"doi":429,"paper":430,"created_at":444},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":113,"substance":178,"depth":114,"authority":134,"freshness":13,"relevant":22,"comment":401},"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。",[403],{"name":273,"url":396},[27,28,282,405,406],"作物病害识别","模型审计",{"code":285,"slug":286,"name":287,"path":288,"parent_code":289},[409],{"code":40,"slug":124,"name":125,"path":126,"parent_code":127},{"status":130,"locked":14,"version":35,"method":36,"evidence":411},{"candidates":412,"agricultural_evidence":42},[413,417,421],{"code":285,"score":17,"supported":42,"title":414,"abstract":415,"tags":416},[297],[282,297],[282],{"code":40,"score":13,"supported":42,"title":418,"abstract":419,"tags":420},[302],[],[405,28],{"code":58,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[27],[426,427],"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":429,"openalex_id":431,"authors":432,"venue":273,"cited_by_count":91,"oa_url":396,"card":439,"direction":97,"ingested_from":99},"W7223088030",[433,435,437],{"name":434,"orcid":9},"Abdi-Basid Idriss Farah",{"name":436,"orcid":9},"Amina Abdillahi Guedi",{"name":438,"orcid":9},"Moussa Omar Youssouf",{"tldr":440,"method":441,"finding":442,"direction":97,"opportunity":443},"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"]