[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4907":3,"related-4907":88},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":87},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。",null,"Applied Sciences","2026-10-13T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","可解释AI","植物病害识别","豌豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":53},12,true,[],[50,51,52],"深度学习","deep learning","artificial intelligence",[28,29,30],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":61},"I1",7,[],[59,60],"精准农业","precision agriculture",[27],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"A5",[],[],[31],[68,69],"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":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7221612639",[76],{"name":77,"orcid":78},"Fatma Ozge Ozkok","https:\u002F\u002Forcid.org\u002F0000-0002-1421-4670",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"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。","农业人工智能与决策模型","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。","openalex","2026-10-14T23:30:46.091813Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,144,205,261,338,407],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":103,"tags":105,"primary_topic":106,"secondary_topics":107,"classification":108,"search_phrases":126,"slug":129,"view_count":79,"doi":130,"paper":131,"created_at":143},2178,"Hybrid explainable DeiT-based framework for plant disease classification and severity estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02191-2","The detection of plant diseases is essential for preserving agricultural productivity and food security; however, existing approaches often suffer from limited interpretability and generalization capability. This study proposes a hybrid deep learning framework based on Data-efficient Image Transformers (DeiT) for plant disease classification and severity estimation. The framework employs DeiT-Base, DeiT-Small, and DeiT-Tiny models to capture global contextual dependencies in plant leaf images. To improve interpretability, a hybrid Explainable Artificial Intelligence (XAI) module is introduced by combining Gradient-weighted Class Activation Mapping (Grad-CAM) for local feature attribution with Attention Rollout for global dependency visualization. In addition, HSV-based segmentation is applied after classification to isolate disease-relevant regions for damage ratio computation, severity estimation, and explanation refinement. The damage ratio is further integrated with Hybrid XAI attention maps to estimate disease severity. Experiments were conducted on the New Plant Diseases Dataset (Augmented), comprising 70,295 training images and 17,572 validation images across 38 disease classes and 14 plant species. The proposed DeiT-Base model achieved a maximum classification accuracy of 99.13%, outperforming several CNN architectures, including ResNet50, DenseNet121, MobileNetV3, EfficientNet-B4, and InceptionV3. Furthermore, the proposed Hybrid XAI framework demonstrated superior interpretability performance in terms of Focus Score, Background Noise, Signal-to-Noise Ratio (SNR), and Entropy compared with individual explanation methods. Overall, the proposed framework improves classification accuracy, enhances model transparency, and provides meaningful disease severity estimation, making it a promising solution for intelligent precision agriculture.","植物病害检测对于保障农业生产力和粮食安全至关重要，然而现有方法往往存在可解释性有限和泛化能力不足的问题。本研究提出了一种基于数据高效图像Transformer（Data-efficient Image Transformers，DeiT）的混合深度学习框架，用于植物病害分类和严重程度估计。该框架采用DeiT-Base、DeiT-Small和DeiT-Tiny模型来捕获植物叶片图像中的全局上下文依赖关系。为提高可解释性，引入了一种混合可解释人工智能（Explainable Artificial Intelligence，XAI）模块，将用于局部特征归因的梯度加权类激活映射（Gradient-weighted Class Activation Mapping，Grad-CAM）与用于全局依赖可视化的注意力展开（Attention Rollout）相结合。此外，在分类之后应用基于HSV的分割来分离病害相关区域，以进行损伤比率计算、严重程度估计和解释优化。损伤比率进一步与混合XAI注意力图相结合以估计病害严重程度。实验在新植物病害数据集（增强版）（New Plant Diseases Dataset (Augmented)）上进行，该数据集包含70,295张训练图像和17,572张验证图像，涵盖38个病害类别和14种植物物种。所提出的DeiT-Base模型达到了99.13%的最高分类准确率，优于多种CNN架构，包括ResNet50、DenseNet121、MobileNetV3、EfficientNet-B4和InceptionV3。此外，所提出的混合XAI框架在聚焦分数（Focus Score）、背景噪声（Background Noise）、信噪比（Signal-to-Noise Ratio，SNR）和熵（Entropy）方面表现出优于单一解释方法的可解释性性能。总体而言，所提出的框架提高了分类准确率，增强了模型透明度，并提供了有意义的病害严重程度估计，使其成为智能精准农业的一种有前景的解决方案。","Discover Artificial Intelligence","2026-09-10T00:00:00Z",78,{"impact":18,"substance":101,"depth":18,"authority":46,"freshness":17,"relevant":22,"comment":102},22,"基于DeiT与混合可解释AI的植物病害分类与严重度估计研究，在7万张图像、38类病害上取得99.13%准确率，方法新颖、数据扎实，对智慧农业病害智能诊断有参考价值。",[104],{"name":97,"url":94},[27,28,29,59,30],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":109},{"candidates":110,"agricultural_evidence":47},[111,115,119],{"code":33,"score":46,"supported":47,"title":112,"abstract":113,"tags":114},[],[50,51,52],[28,29,30],{"code":55,"score":17,"supported":47,"title":116,"abstract":117,"tags":118},[],[59,60],[27,59],{"code":120,"score":89,"supported":47,"title":121,"abstract":122,"tags":125},"A8",[],[123,124],"粮食安全","food security",[],[127,128],"农业人工智能 植物病害识别 智慧农业 精准农业","农业人工智能 植物病害识别","农业人工智能植物病害识别智慧农业精准农业-2178","10.1007\u002Fs44163-026-02191-2",{"doi":130,"openalex_id":132,"authors":133,"venue":97,"cited_by_count":79,"oa_url":94,"card":138,"direction":84,"ingested_from":86},"W7212179384",[134,136],{"name":135,"orcid":9},"Muskan Batra",{"name":137,"orcid":9},"Pooja Sharma",{"tldr":139,"method":140,"finding":141,"direction":84,"opportunity":142},"提出基于DeiT的混合可解释框架，实现植物病害分类与严重度估计。","DeiT-Base\u002FSmall\u002FTiny结合Grad-CAM与Attention","DeiT-Base分类准确率达99.13%，混合XAI可解释性指标优于单一方法。","可探索轻量化DeiT在边缘设备部署及多模态数据融合的病害严重度实时估计。","2026-09-11T23:30:47.756846Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":9,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":156,"tags":158,"primary_topic":161,"secondary_topics":167,"classification":169,"search_phrases":186,"slug":188,"view_count":79,"doi":189,"paper":190,"created_at":204},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":17,"substance":154,"depth":18,"authority":46,"freshness":13,"relevant":22,"comment":155},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。",[157],{"name":150,"url":147},[27,28,159,30,160],"小农户","可解释性",{"code":162,"slug":163,"name":164,"path":165,"parent_code":166},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[168],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":170},{"candidates":171,"agricultural_evidence":47},[172,177,182],{"code":162,"score":46,"supported":47,"title":173,"abstract":175,"tags":176},[174],"smallholder",[174],[159],{"code":33,"score":13,"supported":47,"title":178,"abstract":180,"tags":181},[179],"computer vision",[],[28,30],{"code":55,"score":22,"supported":14,"title":183,"abstract":184,"tags":185},[],[],[27],[187,128],"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可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":189,"openalex_id":191,"authors":192,"venue":150,"cited_by_count":79,"oa_url":147,"card":199,"direction":84,"ingested_from":86},"W7223137323",[193,195,197],{"name":194,"orcid":9},"Akshay Ramgoolam",{"name":196,"orcid":9},"Priya Bundhoo Appadoo",{"name":198,"orcid":9},"Jean-Noël LEspérance",{"tldr":200,"method":201,"finding":202,"direction":84,"opportunity":203},"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":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":9,"source_name":211,"source_url":208,"published_at":212,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":213,"sources":216,"tags":218,"primary_topic":221,"secondary_topics":222,"classification":223,"search_phrases":241,"slug":244,"view_count":79,"doi":245,"paper":246,"created_at":260},5192,"A Comparative Study of Client-Level Stabilization Techniques in Federated Learning for Robust Plant Disease Recognition","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206566","The successful integration of artificial intelligence (AI) algorithms in precision agriculture provided farmers with the means to identify potential issues, such as early disease detection or pest infestations, by making crop-related predictions. The new technological developments in this field improve the functioning of traditional agriculture by reducing the dependence on specialized human expertise and facilitating greater access to diagnostic procedures. However, most studies conducted in this area rely on centralized training of AI models, which leads to increased costs and raised privacy-related concerns. Federated learning (FL) offers a compelling framework for collaboratively training machine learning (ML) models on distributed datasets without centralizing the data owned by each participant. FL offers significant advantages in the preservation of data privacy, facilitates the collaborative exploitation of geographically distributed data sources, and reduces the risks associated with centralized collection. In this paper, we investigate the current limitations and performance boundaries of existing Federated and privacy-preserving learning in distributed sensing systems for plant disease detection, in scenarios that closely resemble real-world conditions. Our analysis specifically examined how FL aggregation algorithms, such as FedAvg, FedAvgM and FedProx, work in a custom multi-client experimental setup where data is partitioned so that each participant observes only those symptom patterns associated with specific environmental and climatic conditions. We propose further partial aggregation of the local model, an approach where we send only the model backbone to the central server, while the classification head of each participant remains strictly on the client side. The construction of environmentally heterogeneity is combined with a globally representative stabilizer node designed to preserve knowledge across disease classes that may be absent from certain clients.","人工智能（AI）算法在精准农业中的成功集成，为农民提供了通过作物相关预测来识别潜在问题（如早期病害检测或虫害侵染）的手段。该领域的新技术发展通过减少对专业人类知识的依赖并促进更广泛的诊断程序获取，改善了传统农业的运作方式。然而，该领域的大多数研究依赖于AI模型的集中式训练，这导致成本增加并引发隐私相关担忧。联邦学习（FL）提供了一个引人注目的框架，可在分布式数据集上协作训练机器学习（ML）模型，而无需集中化各参与者拥有的数据。联邦学习在保护数据隐私方面具有显著优势，促进了地理上分布的数据源的协作利用，并降低了与集中收集相关的风险。本文研究了在高度接近真实条件的场景中，现有联邦学习与隐私保护学习在用于植物病害检测的分布式感知系统中的当前局限性与性能边界。我们的分析特别考察了联邦学习聚合算法（如FedAvg、FedAvgM和FedProx）在自定义多客户端实验设置中的工作方式，其中数据被划分，使得每个参与者仅观察到与特定环境和气候条件相关的症状模式。我们提出对本地模型进行进一步的部分聚合，即仅将模型骨干发送至中央服务器，而每个参与者的分类头严格保留在客户端侧。环境异质性的构建与一个全局代表性的稳定器节点相结合，该节点旨在保留某些客户端可能缺失的跨病害类别的知识。","Sensors","2026-10-16T00:00:00Z",{"impact":18,"substance":154,"depth":214,"authority":20,"freshness":21,"relevant":22,"comment":215},17,"该研究针对植物病害识别中的联邦学习客户端稳定化方法提出部分聚合与稳定节点方案，方法新颖、实验贴近真实场景，对智慧农业隐私计算有参考价值，值得进入每日精选。",[217],{"name":211,"url":208},[27,28,219,30,220],"联邦学习","数据隐私",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":224},{"candidates":225,"agricultural_evidence":47},[226,232,236],{"code":33,"score":46,"supported":47,"title":227,"abstract":228,"tags":231},[],[229,230,52],"机器学习","machine learning",[28,30,219],{"code":55,"score":56,"supported":47,"title":233,"abstract":234,"tags":235},[],[59,60],[27],{"code":237,"score":22,"supported":14,"title":238,"abstract":239,"tags":240},"I6",[],[],[220],[242,243],"联邦学习 植物病害识别","FedProx 作物病害检测","联邦学习植物病害识别-5192","10.3390\u002Fs26206566",{"doi":245,"openalex_id":247,"authors":248,"venue":211,"cited_by_count":79,"oa_url":208,"card":255,"direction":84,"ingested_from":86},"W7222586734",[249,252],{"name":250,"orcid":251},"Aurora-Felicia Cristea","https:\u002F\u002Forcid.org\u002F0009-0003-9718-065X",{"name":253,"orcid":254},"Ciprian Dobre","https:\u002F\u002Forcid.org\u002F0000-0003-4638-7725",{"tldr":256,"method":257,"finding":258,"direction":84,"opportunity":259},"比较联邦学习客户端级稳定化技术，提升植物病害识别的鲁棒性。","FedAvg\u002FFedAvgM\u002FFedProx聚合，仅上传骨干网络、分类头留本地，","部分聚合加稳定节点可缓解环境异质性与类别缺失带来的性能退化。","可探索面向非独立同分布农业数据的个性化联邦学习与通信效率优化。","2026-10-17T23:30:45.845684Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":274,"primary_topic":276,"secondary_topics":277,"classification":278,"search_phrases":303,"slug":306,"view_count":79,"doi":307,"paper":308,"created_at":337},5080,"Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM","https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088467x261495708","Early and accurate detection of plant diseases is essential for improving crop productivity and supporting sustainable agricultural practices. Mango (Mangifera indica), one of the most economically important tropical fruit crops worldwide, is highly susceptible to several leaf diseases that can significantly reduce yield and fruit quality. This study presents an explainable deep learning framework for automated ten-class mango leaf disease classification using a publicly available dataset containing approximately 5000 images distributed across Anthracnose, Bacterial Canker, Cutting Weevil, Dag Disease, Die Back, Galls, Healthy, Powdery Mildew, Red Rust, and Sooty Mould classes. Unlike conventional AlexNet-based approaches, the proposed framework integrates Bayesian hyperparameter optimization, two-stage fine-tuning, test-time augmentation (TTA), and Grad-CAM-based explainability to improve both predictive performance and interpretability. Experimental results demonstrate that the proposed framework achieves a final test accuracy of 99.2% using eight-view test-time augmentation (TTA), with corresponding precision, recall, and F1-score of approximately 99.1%. The model also achieves micro-average and macro-average ROC–AUC scores of 1.00 on the curated test set, indicating strong discriminative capability across the considered disease categories. Comparative evaluations against MobileNetV2, DenseNet201, ConvNeXt, and LeafNet demonstrate competitive or superior classification performance while maintaining moderate computational complexity and architectural simplicity. Furthermore, Grad-CAM visualizations qualitatively highlight biologically relevant disease symptoms, including lesions, necrotic regions, rust spots, fungal growth, and tissue discoloration, thereby enhancing model interpretability. Although field-scale validation and quantitative explainability assessment remain subjects for future investigation, the proposed framework provides an accurate, computationally efficient, and interpretable approach for automated mango leaf disease diagnosis with strong potential for smart agriculture and precision farming applications.","植物病害的早期准确检测对于提高作物生产力和支持可持续农业实践至关重要。芒果（Mangifera indica）是全球最具经济重要性的热带水果作物之一，极易感染多种叶部病害，这些病害可显著降低产量和果实品质。本研究提出了一种可解释的深度学习框架，用于自动化十类芒果叶部病害分类，所使用的公开数据集包含约5000张图像，分布于炭疽病（Anthracnose）、细菌性溃疡病（Bacterial Canker）、切叶象甲（Cutting Weevil）、枯枝病（Dag Disease）、回枯病（Die Back）、虫瘿（Galls）、健康（Healthy）、白粉病（Powdery Mildew）、红锈病（Red Rust）和煤烟病（Sooty Mould）十个类别。与传统的基于AlexNet的方法不同，所提出的框架集成了贝叶斯超参数优化、两阶段微调、测试时增强（TTA）以及基于Grad-CAM的可解释性，以同时提升预测性能和可解释性。实验结果表明，所提出的框架在使用八视图测试时增强（TTA）时达到了99.2%的最终测试准确率，相应的精确率、召回率和F1分数约为99.1%。该模型在精选测试集上还取得了1.00的微平均和宏平均ROC–AUC分数，表明在所考虑的病害类别间具有较强的判别能力。与MobileNetV2、DenseNet201、ConvNeXt和LeafNet的对比评估表明，该框架在保持适中计算复杂度和架构简洁性的同时，具有竞争性或更优的分类性能。此外，Grad-CAM可视化定性地突出了具有生物学相关性的病害症状，包括病斑、坏死区域、锈斑、真菌生长和组织变色，从而增强了模型的可解释性。尽管田间规模验证和定量可解释性评估仍有待未来研究，但所提出的框架为自动化芒果叶部病害诊断提供了一种准确、计算高效且可解释的方法，在智慧农业和精准农业应用中具有强大潜力。","Intelligent Data Analysis","2026-10-15T00:00:00Z",76,{"impact":19,"substance":154,"depth":214,"authority":20,"freshness":21,"relevant":22,"comment":271},"将贝叶斯调参、两阶段微调与Grad-CAM结合用于十类芒果叶病识别，准确率99.2%，方法新颖且可解释性强，对智慧农业植保应用有参考价值。",[273],{"name":267,"url":264},[27,28,29,59,275],"芒果病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":279},{"candidates":280,"agricultural_evidence":47},[281,285,291,297],{"code":33,"score":19,"supported":47,"title":282,"abstract":283,"tags":284},[51],[50,51],[28,29],{"code":55,"score":286,"supported":47,"title":287,"abstract":288,"tags":290},11,[],[27,59,289],"smart agriculture",[27,59],{"code":292,"score":71,"supported":47,"title":293,"abstract":294,"tags":296},"B2",[],[295],"芒果",[],{"code":298,"score":71,"supported":47,"title":299,"abstract":300,"tags":302},"H8",[],[301],"可持续农业",[],[304,305],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080","10.1177\u002F1088467x261495708",{"doi":307,"openalex_id":309,"authors":310,"venue":267,"cited_by_count":79,"oa_url":9,"card":331,"direction":336,"ingested_from":86},"W7222328160",[311,314,317,319,322,325,328],{"name":312,"orcid":313},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":315,"orcid":316},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":318,"orcid":9},"Md Moniruzzaman",{"name":320,"orcid":321},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":323,"orcid":324},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":326,"orcid":327},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":329,"orcid":330},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":332,"method":333,"finding":334,"direction":84,"opportunity":335},"提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。","AlexNet+贝叶斯超参优化、两阶段微调、八视图TTA、Grad-CAM，约5","测试准确率达99.2%，ROC-AUC为1.00，Grad-CAM能定位病斑等生物相关症状。","可延伸至田间复杂背景验证、可解释性定量评估及轻量化边缘部署研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:08.303796Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":345,"sources":348,"tags":350,"primary_topic":353,"secondary_topics":354,"classification":355,"search_phrases":386,"slug":389,"view_count":22,"doi":390,"paper":391,"created_at":406},5076,"Trustworthy Explainable AI for Lettuce Growth Stage Prediction Using Sensor Data in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1452.v1","Background In precision agriculture, especially in hydroponic systems where environmental management affects both production and resource efficiency, interpretable modeling of crop phenological stages is crucial. Typical visualization-based approaches are generally expensive and ecologically dependent. To classify and visualize lettuce growth stages (Seedling, Vegetative Growth, Heading, Bolting and Senescence) using temperature, humidity, total dissolved solids (TDS) and pH, this study proposed XGB-StageVision, a sensor-powered machine learning setup combining supervised and unsupervised learning techniques. Using XG Boost, LightGBM, CatBoost, ExtraTrees, Random Forest and LSTM under an 80\u002F20 train-test split, a dataset of 3,169 unique observations from 70 hydroponically grown lettuce plants was evaluated. Methods and Results XGB-StageVision achieved 99.86% test accuracy, a ROC AUC of 1.00, an MCC of 0.9982 and perfect precision, recall, F1 scores and specificity across all classes, performing competitively with the best baseline models, including LightGBM, which achieved statistically comparable cross validation performance. The unsupervised t-SNE visualization showed clearly distinct structural clusters across growth stages, while SHAP revealed temperature as the strongest predictor (≈75% contribution), followed by TDS. Novel Contributions SHAP, LIME and t-SNE provided global, local and structural interpretability and transparent perspectives on environmental influences regarding plant development. Statistical analysis and 10-fold cross-validation were used to further test the model’s robustness, reliability and generalizability. Conclusion XGB-StageVision provides an IoT-ready, interpretable and imaging-free system for monitoring lettuce growth stages in real time. The framework combines supervised predictive modeling with unsupervised structural visualization to ensure transparency, sustainability and high performance, supporting resource-efficient, data-driven strategic planning in contemporary hydroponic agriculture to lower monitoring costs, optimize inputs and improve yield consistency.","背景 在精准农业中，尤其是在环境管理同时影响产量和资源效率的水培系统中，作物物候阶段的可解释建模至关重要。典型的基于可视化的方法通常成本高昂且依赖生态条件。为了利用温度、湿度、总溶解固体（TDS）和pH对生菜生长阶段（幼苗期、营养生长期、结球期、抽薹期和衰老期）进行分类和可视化，本研究提出了XGB-StageVision，一种由传感器驱动的机器学习方案，结合了监督学习和无监督学习技术。在80\u002F20的训练-测试划分下，使用XGBoost、LightGBM、CatBoost、ExtraTrees、随机森林和LSTM，对来自70株水培生菜的3，169个独特观测值数据集进行了评估。方法与结果 XGB-StageVision达到了99.86%的测试准确率、1.00的ROC AUC、0.9982的MCC，并在所有类别上实现了完美的精确率、召回率、F1分数和特异性，与最佳基线模型表现相当，包括LightGBM，其交叉验证性能在统计上具有可比性。无监督t-SNE可视化显示了各生长阶段之间明显不同的结构聚类，而SHAP揭示温度是最强的预测因子（约75%贡献），其次是TDS。新颖贡献 SHAP、LIME和t-SNE提供了全局、局部和结构可解释性，以及关于环境对植物发育影响的可透明视角。使用统计分析和10折交叉验证进一步检验了模型的稳健性、可靠性和泛化性。结论 XGB-StageVision提供了一种物联网就绪、可解释且无需成像的系统，用于实时监测生菜生长阶段。该框架将监督预测建模与无监督结构可视化相结合，以确保透明度、可持续性和高性能，支持当代水培农业中资源高效、数据驱动的战略规划，以降低监测成本、优化投入并提高产量一致性。","Preprints.org",{"impact":46,"substance":346,"depth":214,"authority":89,"freshness":21,"relevant":22,"comment":347},20,"预印本论文，方法新颖、数据规模可观且强调可解释性，但尚未经同行评审，属细分领域技术进展。",[349],{"name":344,"url":341},[27,28,29,351,352],"水培生菜","传感器监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":356},{"candidates":357,"agricultural_evidence":47},[358,363,367,374,380],{"code":55,"score":359,"supported":47,"title":360,"abstract":361,"tags":362},15,[289],[59,60],[27],{"code":33,"score":17,"supported":47,"title":364,"abstract":365,"tags":366},[],[229,230],[28,29],{"code":368,"score":89,"supported":47,"title":369,"abstract":370,"tags":373},"I5",[],[371,372],"物联网","IoT",[],{"code":375,"score":71,"supported":47,"title":376,"abstract":377,"tags":379},"B1",[],[378],"生菜",[],{"code":381,"score":71,"supported":47,"title":382,"abstract":383,"tags":385},"B7",[],[384],"hydroponic",[],[387,388],"XGB-StageVision 生菜 生长阶段","水培生菜 传感器 生长预测","XGB-StageVision生菜生长阶段-5076","10.20944\u002Fpreprints202610.1452.v1",{"doi":390,"openalex_id":392,"authors":393,"venue":344,"cited_by_count":79,"oa_url":341,"card":401,"direction":336,"ingested_from":86},"W7222080602",[394,396,399],{"name":395,"orcid":9},"Muhammad Majid",{"name":397,"orcid":398},"Attia Ghafoor","https:\u002F\u002Forcid.org\u002F0009-0000-3828-6479",{"name":400,"orcid":9},"Amna Munir",{"tldr":402,"method":403,"finding":404,"direction":336,"opportunity":405},"提出XGB-StageVision，用传感器数据与可解释AI预测水培生菜生长阶段。","XGBoost等模型结合SHAP、LIME、t-SNE，基于3169条温湿度TD","测试准确率99.86%，温度贡献约75%，t-SNE显示各生长阶段聚类清晰。","可探索多作物、多环境迁移与低成本传感器部署下的可解释模型泛化能力。","2026-10-16T23:30:08.020788Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":412,"content":9,"source_name":413,"source_url":410,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":418,"tags":420,"primary_topic":423,"secondary_topics":429,"classification":431,"search_phrases":453,"slug":456,"view_count":22,"doi":457,"paper":458,"created_at":483},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports",72,{"impact":46,"substance":346,"depth":18,"authority":416,"freshness":17,"relevant":22,"comment":417},14,"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[419],{"name":413,"url":410},[27,28,29,421,422],"农业气象","水稻产量预测",{"code":424,"slug":425,"name":426,"path":427,"parent_code":428},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[430],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":432},{"candidates":433,"agricultural_evidence":47},[434,440,444,449],{"code":33,"score":435,"supported":47,"title":436,"abstract":437,"tags":439},19,[230],[229,230,438],"decision support",[28,29],{"code":424,"score":416,"supported":47,"title":441,"abstract":442,"tags":443},[425],[426,425],[],{"code":445,"score":22,"supported":14,"title":446,"abstract":447,"tags":448},"H7",[],[],[421],{"code":55,"score":22,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[27],[454,455],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":457,"openalex_id":459,"authors":460,"venue":413,"cited_by_count":79,"oa_url":477,"card":478,"direction":336,"ingested_from":86},"W7221657053",[461,463,465,467,469,471,474],{"name":462,"orcid":9},"Noh-Hyun Lee",{"name":464,"orcid":9},"Wonjae Jeong",{"name":466,"orcid":9},"Hyeon-Ji Yang",{"name":468,"orcid":9},"Hana Cho",{"name":470,"orcid":9},"Hyeon-Yeong Ju",{"name":472,"orcid":473},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":475,"orcid":476},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":479,"method":480,"finding":481,"direction":84,"opportunity":482},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z"]