[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3054":3,"related-3054":99},{"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":98},3054,"Assessment of mechanical damage in canola using X-ray radiography and deep learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112350","Mechanical damage during post-harvest handling and processing can drastically impair the quality, germination potential, and market value of canola ( Brassica napus L.) seeds. Traditional visual inspection methods are subjective and limited to detecting external defects, often missing internal damage. This study presents a non-destructive approach for classifying mechanical damage in canola seeds using X-ray radiography combined with artificial intelligence techniques. Seeds at three moisture contents (6 %, 8 %, and 12 %) were subjected to four levels of impact energy (0, 3, 3.7, and 4.3 mJ), generating 3,029 radiographic images labelled into four levels of varying mechanical damage (no damage, low damage, medium damage, and high damage). Two analytical pipelines were developed. The first involved machine learning with 37 extracted features, including intensity-based metrics, texture descriptors, frequency-domain features, and Fast Fourier Transform (FFT) features. Support vector machine (SVM) and random forest (RF) models were developed and evaluated; the SVM achieved the highest accuracy of 74.01 % after feature selection. The second pipeline implemented deep learning via transfer learning on five pre-trained convolutional neural networks: MobileNetV2, EfficientNet-B0, Xception, ResNet50, and DenseNet121. Among these, MobileNetV2 achieved the highest accuracy of 91.42 %, outperforming all traditional machine learning models. The results demonstrate the potential of integrating radiographic imaging with artificial intelligence methods for rapid, reliable, and non-destructive classification of mechanical damage in canola seeds, paving the way for intelligent quality control systems in the oilseed supply chain.","油菜（Brassica napus L.）种子在采后处理和加工过程中的机械损伤会严重损害其品质、发芽潜力和市场价值。传统的视觉检测方法主观性强，且仅限于检测外部缺陷，往往无法发现内部损伤。本研究提出了一种利用X射线成像结合人工智能技术对油菜种子机械损伤进行无损分类的方法。将三种含水率（6%、8%和12%）的种子分别施加四个水平的冲击能量（0、3、3.7和4.3 mJ），共生成3,029张射线图像，并标注为四个不同程度的机械损伤等级（无损伤、低损伤、中等损伤和高损伤）。研究开发了两条分析流程。第一条采用机器学习方法，提取了37个特征，包括基于强度的指标、纹理描述符、频域特征以及快速傅里叶变换（FFT）特征，开发并评估了支持向量机（SVM）和随机森林（RF）模型，其中SVM在特征选择后达到了74.01%的最高准确率。第二条流程通过迁移学习在五个预训练卷积神经网络上实现深度学习：MobileNetV2、EfficientNet-B0、Xception、ResNet50和DenseNet121。其中，MobileNetV2达到了91.42%的最高准确率，优于所有传统机器学习模型。结果表明，将射线成像与人工智能方法相结合，有望实现对油菜种子机械损伤的快速、可靠且无损的分类，为油料种子供应链中的智能质量控制系统的建立奠定了基础。",null,"Computers and Electronics in Agriculture","2026-09-20T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"将X射线成像与深度学习结合用于油菜籽内部机械损伤无损分级，方法新颖、数据规模可观，对油料供应链智能质控有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","无损检测","油菜","种子质量",{"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":45},[44,54,60],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":53},true,[47],"deep learning",[49,50,51,47,52],"深度学习","机器学习","machine learning","artificial intelligence",[27],{"code":55,"score":56,"supported":45,"title":57,"abstract":58,"tags":59},"A4",4,[],[29],[29],{"code":61,"score":21,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[26],[66,67],"X射线 油菜籽 机械损伤","MobileNetV2 种子 无损检测","X射线油菜籽机械损伤-3054",2,"10.1016\u002Fj.compag.2026.112350",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7213778300",[74,76,79,81,84,87],{"name":75,"orcid":9},"M. Buragohain",{"name":77,"orcid":78},"L. G. Divyanth","https:\u002F\u002Forcid.org\u002F0000-0002-1121-1642",{"name":80,"orcid":9},"Taranveer Singh",{"name":82,"orcid":83},"Muhammad Mudassir Arif Chaudhry","https:\u002F\u002Forcid.org\u002F0000-0001-5316-4701",{"name":85,"orcid":86},"Jitendra Paliwal","https:\u002F\u002Forcid.org\u002F0000-0002-1665-3626",{"name":88,"orcid":89},"Mohammad Nadimi","https:\u002F\u002Forcid.org\u002F0000-0002-4550-7572",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"用X射线成像结合机器学习与深度学习，无损分类油菜籽机械损伤等级。","3029张X射线图像，提取37个特征训练SVM\u002FRF，并用五种预训练CNN迁移学","MobileNetV2准确率达91.42%，显著优于传统机器学习最高74.01%。","农业人工智能与决策模型","可拓展至多作物种子内部损伤实时检测，并融合高光谱或近红外实现便携式在线分选。","openalex","2026-09-21T23:30:02.090869Z",{"total":100,"page":21,"page_size":100,"items":101},6,[102,208,272,333,397,449],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":115,"tags":117,"primary_topic":120,"secondary_topics":125,"classification":127,"search_phrases":181,"slug":184,"view_count":90,"doi":185,"paper":186,"created_at":207},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":112,"substance":17,"depth":113,"authority":20,"freshness":13,"relevant":21,"comment":114},12,16,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[116],{"name":108,"url":105},[26,27,28,118,119],"多光谱成像","食品质量检测",{"code":121,"slug":122,"name":123,"path":124,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[126],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":128},{"candidates":129,"agricultural_evidence":45},[130,135,141,149,156,163,170,177],{"code":121,"score":112,"supported":45,"title":131,"abstract":133,"tags":134},[132],"multispectral",[132],[118],{"code":32,"score":13,"supported":45,"title":136,"abstract":137,"tags":140},[],[49,50,138,51,47,52,139],"计算机视觉","computer vision",[27],{"code":142,"score":143,"supported":45,"title":144,"abstract":145,"tags":148},"E4",7,[],[146,147],"食品安全","food safety",[28],{"code":150,"score":100,"supported":45,"title":151,"abstract":152,"tags":155},"A1",[],[153,154],"稻米","rice",[],{"code":157,"score":100,"supported":45,"title":158,"abstract":159,"tags":162},"B1",[],[160,161],"番茄","tomato",[],{"code":164,"score":100,"supported":45,"title":165,"abstract":166,"tags":169},"B2",[],[167,168],"苹果","apple",[],{"code":171,"score":172,"supported":45,"title":173,"abstract":174,"tags":176},"E1",3,[],[175],"农产品加工",[],{"code":61,"score":21,"supported":14,"title":178,"abstract":179,"tags":180},[],[],[26],[182,183],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":185,"openalex_id":187,"authors":188,"venue":108,"cited_by_count":90,"oa_url":105,"card":201,"direction":206,"ingested_from":97},"W7223139905",[189,191,193,195,197,199],{"name":190,"orcid":9},"Eshwari Take",{"name":192,"orcid":9},"Avanish Raut",{"name":194,"orcid":9},"Vedant Joshi",{"name":196,"orcid":9},"Shravani Akolkar",{"name":198,"orcid":9},"R. M. Gharat",{"name":200,"orcid":9},"Shruti Dive",{"tldr":202,"method":203,"finding":204,"direction":95,"opportunity":205},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":221,"tags":223,"primary_topic":226,"secondary_topics":227,"classification":228,"search_phrases":250,"slug":253,"view_count":69,"doi":254,"paper":255,"created_at":271},3017,"Automated seedling vigor estimation in cucumbers using digital image processing","https:\u002F\u002Fdoi.org\u002F10.65764\u002Ftjas.2026.267412","Background and Objective: Farmers often rely on experience rather than quantitative indicators to determine seedling quality, resulting in inefficiencies in resource allocation. This study aimed to (1) compare seedling vigor and growth characteristics between open-pollinated (OP) and F1 hybrid cucumber seed types, and (2) develop a non-destructive predictive model for seedling vigor estimation based on digital image analysis.Methodology: Seedling images were acquired under controlled LED lighting using a 5 MP digital camera positioned 30 cm above 14-day-old seedlings. A YOLOv8-based object detection model was applied to detect and isolate true leaf regions, from which pixel count and RGB color values were extracted as input features for a multiple linear regression model to predict the seedling vigor index (SVI).Main Results: The YOLOv8 object detection model achieved a mean average precision (mAP@0.5) of 96.00%, precision of 93.40%, and recall of 94.40% in detecting true leaves. Multiple linear regression analysis was conducted using the Scikit-learn library in Python. Scikit-learn provides regression-based machine learning algorithms, including multiple linear regression. The equation was then applied to the training and test sets, using the pixel values of true leaves as the independent variable and SVI as the dependent variable. Using multiple regression analysis, the trained model generated the equation SVI = -2.27 + (2.84 × 10-5 × pixel) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B), R2 = 0.57 and RMSE = 1.10. The model achieved the test set, r = 0.79 and RMSE = 1.34. The model performance showed a correlation coefficient of 0.85 for OP data and 0.91 for F1 hybrid data.Conclusions: The results demonstrate that digital image processing combined with object detection provides a non-destructive and effective approach to estimate seedling vigor quality. The predictive models developed from OP and F1 hybrid datasets indicate potential application in precision agriculture for automated seedling quality assessment and transplanting decision support.","背景与目标：农民通常依赖经验而非定量指标来判断幼苗质量，导致资源配置效率低下。本研究旨在（1）比较开放授粉（OP）与F1杂交黄瓜种子类型之间的幼苗活力和生长特性，（2）基于数字图像分析开发一种用于幼苗活力评估的无损预测模型。方法：使用500万像素数码相机置于14日龄幼苗上方30 cm处，在受控LED光照下获取幼苗图像。应用基于YOLOv8的目标检测模型检测并分离真叶区域，从中提取像素计数和RGB颜色值作为多元线性回归模型的输入特征，以预测幼苗活力指数（SVI）。主要结果：YOLOv8目标检测模型在检测真叶时达到了96.00%的平均精度均值（mAP@0.5）、93.40%的精确率和94.40%的召回率。使用Python中的Scikit-learn库进行多元线性回归分析。Scikit-learn提供基于回归的机器学习算法，包括多元线性回归。随后将该方程应用于训练集和测试集，以真叶像素值作为自变量，SVI作为因变量。通过多元回归分析，训练模型生成的方程为SVI = -2.27 + (2.84 × 10-5 × 像素) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B)，R2 = 0.57，RMSE = 1.10。该模型在测试集上达到r = 0.79，RMSE = 1.34。模型性能显示，OP数据的相关系数为0.85，F1杂交数据的相关系数为0.91。结论：结果表明，数字图像处理结合目标检测为评估幼苗活力质量提供了一种无损且有效的方法。基于OP和F1杂交数据集开发的预测模型表明，其在精准农业中具有用于自动化幼苗质量评估和移栽决策支持的潜在应用。","Thai Journal of Agricultural Science","2026-09-19T00:00:00Z",71,{"impact":112,"substance":218,"depth":219,"authority":112,"freshness":20,"relevant":21,"comment":220},21,17,"基于YOLOv8与多元回归的黄瓜幼苗活力无损估测，方法具体、指标完整，对智慧育苗有参考价值，但属细分作物研究，影响范围有限。",[222],{"name":214,"url":211},[26,27,28,224,225],"图像识别","黄瓜育苗",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":229},{"candidates":230,"agricultural_evidence":45},[231,238,244],{"code":32,"score":232,"supported":45,"title":233,"abstract":234,"tags":237},11,[],[235,50,51,236],"目标检测","decision support",[27,224],{"code":61,"score":143,"supported":45,"title":239,"abstract":240,"tags":243},[],[241,242],"精准农业","precision agriculture",[26],{"code":157,"score":100,"supported":45,"title":245,"abstract":246,"tags":249},[],[247,248],"黄瓜","cucumber",[],[251,252],"黄瓜 幼苗活力 图像处理","YOLOv8 幼苗 检测","黄瓜幼苗活力图像处理-3017","10.65764\u002Ftjas.2026.267412",{"doi":254,"openalex_id":256,"authors":257,"venue":214,"cited_by_count":90,"oa_url":211,"card":266,"direction":206,"ingested_from":97},"W7213631983",[258,260,262,264],{"name":259,"orcid":9},"Thanabodee Withunchettanan",{"name":261,"orcid":9},"Raksak Sermsak",{"name":263,"orcid":9},"Pichittra Kaewsorn",{"name":265,"orcid":9},"Kriengkri Kaewtrakulpong",{"tldr":267,"method":268,"finding":269,"direction":95,"opportunity":270},"用YOLOv8检测黄瓜真叶并结合多元回归，实现幼苗活力指数无损预测。","LED下拍摄14天幼苗，YOLOv8分割真叶，提取像素与RGB做多元线性回归。","YOLOv8检测mAP@0.5达96%，模型测试r=0.79，F1杂交种相关性达0.91。","可扩展多品种、多环境数据，融合时序图像与深度学习提升活力预测泛化性。","2026-09-20T23:30:26.929607Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":283,"tags":285,"primary_topic":289,"secondary_topics":295,"classification":297,"search_phrases":313,"slug":316,"view_count":90,"doi":317,"paper":318,"created_at":332},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)",69,{"impact":281,"substance":218,"depth":17,"authority":112,"freshness":13,"relevant":21,"comment":282},8,"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。",[284],{"name":278,"url":275},[26,27,286,287,288],"小农户","植物病害识别","可解释性",{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[296],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":298},{"candidates":299,"agricultural_evidence":45},[300,305,309],{"code":290,"score":112,"supported":45,"title":301,"abstract":303,"tags":304},[302],"smallholder",[302],[286],{"code":32,"score":13,"supported":45,"title":306,"abstract":307,"tags":308},[139],[],[27,287],{"code":61,"score":21,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[26],[314,315],"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":317,"openalex_id":319,"authors":320,"venue":278,"cited_by_count":90,"oa_url":275,"card":327,"direction":95,"ingested_from":97},"W7223137323",[321,323,325],{"name":322,"orcid":9},"Akshay Ramgoolam",{"name":324,"orcid":9},"Priya Bundhoo Appadoo",{"name":326,"orcid":9},"Jean-Noël LEspérance",{"tldr":328,"method":329,"finding":330,"direction":95,"opportunity":331},"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":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":278,"source_url":336,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":339,"sources":341,"tags":343,"primary_topic":347,"secondary_topics":353,"classification":355,"search_phrases":379,"slug":382,"view_count":90,"doi":383,"paper":384,"created_at":396},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":281,"substance":18,"depth":17,"authority":112,"freshness":20,"relevant":21,"comment":340},"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。",[342],{"name":278,"url":336},[26,27,344,345,346],"灌溉决策","安全保证","试验设计",{"code":348,"slug":349,"name":350,"path":351,"parent_code":352},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[354],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":356},{"candidates":357,"agricultural_evidence":45},[358,364,368,375],{"code":348,"score":359,"supported":45,"title":360,"abstract":361,"tags":363},15,[349],[362,349],"灌溉",[344],{"code":32,"score":112,"supported":45,"title":365,"abstract":366,"tags":367},[52],[236],[27],{"code":369,"score":100,"supported":45,"title":370,"abstract":371,"tags":374},"H2",[],[372,373],"土壤","soil",[],{"code":61,"score":21,"supported":14,"title":376,"abstract":377,"tags":378},[],[],[26],[380,381],"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":383,"openalex_id":385,"authors":386,"venue":278,"cited_by_count":90,"oa_url":336,"card":391,"direction":95,"ingested_from":97},"W7223131205",[387,389],{"name":388,"orcid":9},"Nassima Benkhelifa",{"name":390,"orcid":9},"Yacine Aït Ahmed",{"tldr":392,"method":393,"finding":394,"direction":95,"opportunity":395},"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":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":9,"source_name":278,"source_url":400,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":414,"search_phrases":429,"slug":432,"view_count":90,"doi":433,"paper":434,"created_at":448},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":281,"substance":18,"depth":17,"authority":112,"freshness":13,"relevant":21,"comment":405},"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。",[407],{"name":278,"url":400},[26,27,286,409,410],"作物病害识别","模型审计",{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},[413],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":415},{"candidates":416,"agricultural_evidence":45},[417,421,425],{"code":290,"score":359,"supported":45,"title":418,"abstract":419,"tags":420},[302],[286,302],[286],{"code":32,"score":13,"supported":45,"title":422,"abstract":423,"tags":424},[139],[],[409,27],{"code":61,"score":21,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[26],[430,431],"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":433,"openalex_id":435,"authors":436,"venue":278,"cited_by_count":90,"oa_url":400,"card":443,"direction":95,"ingested_from":97},"W7223088030",[437,439,441],{"name":438,"orcid":9},"Abdi-Basid Idriss Farah",{"name":440,"orcid":9},"Amina Abdillahi Guedi",{"name":442,"orcid":9},"Moussa Omar Youssouf",{"tldr":444,"method":445,"finding":446,"direction":95,"opportunity":447},"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":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":278,"source_url":452,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":481,"slug":484,"view_count":90,"doi":485,"paper":486,"created_at":496},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":281,"substance":17,"depth":219,"authority":20,"freshness":13,"relevant":21,"comment":457},"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。",[459],{"name":278,"url":452},[26,27,461,462],"精准施药","病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":466},{"candidates":467,"agricultural_evidence":45},[468,472,476],{"code":32,"score":20,"supported":45,"title":469,"abstract":470,"tags":471},[52],[],[27],{"code":61,"score":143,"supported":45,"title":473,"abstract":474,"tags":475},[],[241,242],[26],{"code":477,"score":21,"supported":14,"title":478,"abstract":479,"tags":480},"F4",[],[],[461],[482,483],"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":485,"openalex_id":487,"authors":488,"venue":278,"cited_by_count":90,"oa_url":452,"card":491,"direction":95,"ingested_from":97},"W7223116087",[489],{"name":490,"orcid":9},"Achieng' Atim Okello",{"tldr":492,"method":493,"finding":494,"direction":95,"opportunity":495},"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"]