[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2178":3,"related-2178":87},{"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":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":86},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。",null,"Discover Artificial Intelligence","2026-09-10T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","可解释AI","精准农业","植物病害识别",{"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},[44,52,58],{"code":32,"score":19,"supported":45,"title":46,"abstract":47,"tags":51},true,[],[48,49,50],"深度学习","deep learning","artificial intelligence",[27,28,30],{"code":53,"score":20,"supported":45,"title":54,"abstract":55,"tags":57},"I1",[],[29,56],"precision agriculture",[26,29],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":65},"A8",6,[],[63,64],"粮食安全","food security",[],[67,68],"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",0,"10.1007\u002Fs44163-026-02191-2",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":70,"oa_url":6,"card":79,"direction":83,"ingested_from":85},"W7212179384",[75,77],{"name":76,"orcid":9},"Muskan Batra",{"name":78,"orcid":9},"Pooja Sharma",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","农业人工智能与决策模型","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。","openalex","2026-09-11T23:30:47.756846Z",{"total":60,"page":21,"page_size":60,"items":88},[89,172,221,272,333,391],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":105,"tags":107,"primary_topic":109,"secondary_topics":110,"classification":111,"search_phrases":137,"slug":140,"view_count":70,"doi":141,"paper":142,"created_at":171},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":99,"substance":100,"depth":101,"authority":102,"freshness":103,"relevant":21,"comment":104},16,21,17,13,9,"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。",[106],{"name":95,"url":92},[26,27,28,29,108],"芒果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":112},{"candidates":113,"agricultural_evidence":45},[114,118,124,131],{"code":32,"score":99,"supported":45,"title":115,"abstract":116,"tags":117},[49],[48,49],[27,28],{"code":53,"score":119,"supported":45,"title":120,"abstract":121,"tags":123},11,[],[26,29,122],"smart agriculture",[26,29],{"code":125,"score":126,"supported":45,"title":127,"abstract":128,"tags":130},"B2",3,[],[129],"芒果",[],{"code":132,"score":126,"supported":45,"title":133,"abstract":134,"tags":136},"H8",[],[135],"可持续农业",[],[138,139],"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":141,"openalex_id":143,"authors":144,"venue":95,"cited_by_count":70,"oa_url":9,"card":165,"direction":170,"ingested_from":85},"W7222328160",[145,148,151,153,156,159,162],{"name":146,"orcid":147},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":149,"orcid":150},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":152,"orcid":9},"Md Moniruzzaman",{"name":154,"orcid":155},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":157,"orcid":158},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":160,"orcid":161},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":163,"orcid":164},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":166,"method":167,"finding":168,"direction":83,"opportunity":169},"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":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":184,"tags":186,"primary_topic":188,"secondary_topics":189,"classification":190,"search_phrases":205,"slug":208,"view_count":21,"doi":209,"paper":210,"created_at":220},4982,"A Nightshade-SWIN Transformer-Based Framework for Enhanced Multi-Crop Plant Disease Classification","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.151037","Nightshade crops (Solanaceae) face significant yield threats from phenotypically similar foliar diseases that can be difficult to distinguish during the early stages of infection.Accurate and timely disease identification is therefore essential for reducing crop losses, improving disease management, and supporting sustainable agricultural practices.While Convolutional Neural Networks (CNNs) have demonstrated strong performance in image-based plant disease classification due to their ability to extract spatial and local features, they may have limitations in capturing long-range dependencies and global contextual relationships within leaf images.These limitations become particularly important when disease symptoms exhibit subtle differences in texture, color, shape, and lesion distribution or when images are captured against complex field backgrounds.To address these challenges, we propose Nightshade-SWIN, an optimized deep learning framework based on the Swin Transformer architecture.The proposed framework utilizes a hierarchical shifted-window self-attention mechanism to effectively capture both minute lesion textures and global leaf morphology.By progressively learning multi-scale representations, Nightshade-SWIN can identify discriminative pathological characteristics while maintaining computational efficiency.The model was evaluated on a curated dataset of 5,000 multicrop images collected under variable field conditions, including differences in illumination, background complexity, leaf orientation, and disease severity.Experimental results demonstrate that Nightshade-SWIN achieves a peak classification accuracy of 94.6%, outperforming ResNet50 and EfficientNet-B0 by 4.9% and 3.4%, respectively.These results indicate the effectiveness of transformer-based feature learning for distinguishing visually similar foliar diseases.Furthermore, interpretability analysis using LIME and Grad-CAM confirms that the model primarily focuses on biologically relevant pathological regions, such as lesions, discoloration, spots, and infected leaf areas.This enhances the transparency and reliability of the proposed framework and supports its potential application in precision agriculture, automated crop monitoring, and early disease diagnosis.","茄科作物（Solanaceae）面临表型相似叶部病害的严重产量威胁，这些病害在感染早期难以区分。因此，准确及时地识别病害对于减少作物损失、改善病害管理以及支持可持续农业实践至关重要。尽管卷积神经网络（CNN）因其提取空间和局部特征的能力而在基于图像的植物病害分类中表现出色，但它们在捕捉叶片图像中的长距离依赖关系和全局上下文关联方面可能存在局限。当病害症状在纹理、颜色、形状和病斑分布上表现出细微差异，或图像在复杂田间背景下拍摄时，这些局限尤为突出。为应对这些挑战，我们提出了Nightshade-SWIN，一种基于Swin Transformer架构的优化深度学习框架。该框架利用分层移位窗口自注意力机制，有效捕捉微小的病斑纹理和全局叶片形态。通过逐步学习多尺度表征，Nightshade-SWIN能够在保持计算效率的同时识别具有判别力的病理特征。该模型在包含5,000张多作物图像的数据集上进行了评估，这些图像采集自多变田间条件，包括光照、背景复杂度、叶片朝向和病害严重程度的差异。实验结果表明，Nightshade-SWIN达到了94.6%的最高分类准确率，分别优于ResNet50和EfficientNet-B0达4.9%和3.4%。这些结果表明了基于Transformer的特征学习在区分视觉相似叶部病害方面的有效性。此外，使用LIME和Grad-CAM进行的可解释性分析证实，该模型主要关注生物学相关的病理区域，如病斑、变色、斑点及受感染叶片区域。这增强了所提框架的透明度和可靠性，并支持其在精准农业、自动化作物监测和早期病害诊断中的潜在应用。","IJARCCE","2026-10-14T00:00:00Z",68,{"impact":19,"substance":182,"depth":101,"authority":13,"freshness":103,"relevant":21,"comment":183},20,"该论文提出基于Swin Transformer的茄科作物病害分类框架，在5000张多作物图像上达到94.6%准确率，方法新颖、数据规模可观，对智慧农业病害识别有参考价值，但属细分领域学术进展，公共影响有限。",[185],{"name":178,"url":175},[26,27,29,30,187],"茄科作物",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":191},{"candidates":192,"agricultural_evidence":45},[193,197,201],{"code":53,"score":20,"supported":45,"title":194,"abstract":195,"tags":196},[],[29,56],[26,29],{"code":32,"score":20,"supported":45,"title":198,"abstract":199,"tags":200},[],[48,49],[27,30],{"code":132,"score":126,"supported":45,"title":202,"abstract":203,"tags":204},[],[135],[],[206,207],"Nightshade-SWIN 病害分类","Swin Transformer 植物病害","Nightshade-SWIN病害分类-4982","10.17148\u002Fijarcce.2026.151037",{"doi":209,"openalex_id":211,"authors":212,"venue":178,"cited_by_count":70,"oa_url":175,"card":215,"direction":170,"ingested_from":85},"W7221746668",[213],{"name":214,"orcid":9},"Dr. Barkha Joshi",{"tldr":216,"method":217,"finding":218,"direction":83,"opportunity":219},"提出Nightshade-SWIN框架，用Swin Transformer提升茄科多作物叶片病害分类","基于Swin Transformer的移位窗口自注意力，5000张田间多作物图像","分类准确率达94.6%，优于ResNet50和EfficientNet-B0，且模型关注病斑等生物相","可探索轻量化Transformer在移动端实时病害诊断及跨作物迁移学习，结合多模态数据提升田间泛化能力。","2026-10-15T23:30:08.177373Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":232,"tags":234,"primary_topic":236,"secondary_topics":237,"classification":238,"search_phrases":255,"slug":258,"view_count":126,"doi":259,"paper":260,"created_at":271},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":20,"substance":17,"depth":99,"authority":102,"freshness":103,"relevant":21,"comment":231},"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。",[233],{"name":227,"url":224},[26,27,28,30,235],"豌豆",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":239},{"candidates":240,"agricultural_evidence":45},[241,245,250],{"code":32,"score":19,"supported":45,"title":242,"abstract":243,"tags":244},[],[48,49,50],[27,28,30],{"code":53,"score":246,"supported":45,"title":247,"abstract":248,"tags":249},7,[],[29,56],[26],{"code":251,"score":21,"supported":14,"title":252,"abstract":253,"tags":254},"A5",[],[],[235],[256,257],"PEAS-DeiT 豌豆叶病害","DeiT 植物病害分类","PEAS-DeiT豌豆叶病害-4907","10.3390\u002Fapp162010113",{"doi":259,"openalex_id":261,"authors":262,"venue":227,"cited_by_count":70,"oa_url":224,"card":266,"direction":83,"ingested_from":85},"W7221612639",[263],{"name":264,"orcid":265},"Fatma Ozge Ozkok","https:\u002F\u002Forcid.org\u002F0000-0002-1421-4670",{"tldr":267,"method":268,"finding":269,"direction":83,"opportunity":270},"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":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":285,"tags":287,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":303,"slug":306,"view_count":21,"doi":307,"paper":308,"created_at":332},4425,"Fuzzy-guided hyperparameter optimization for efficient CNN-based plant disease classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-64880-3","Abstract Deep Convolutional Neural Networks (DCNNs) have exhibited outstanding performance in plant disease classification. Nevertheless, their usefulness is very susceptible to hyperparameter selection. Efficient Hyper-Parameter Optimization (HPO) remains a fundamental challenge in deploying deep learning models for plant disease classification, as implementing extensive search techniques such as grid search, Bayesian Optimization (BO), and evolutionary algorithms requires considerable computational cost. Such conventional HPO approaches frequently rely on single-objective fitness criteria and demand substantial computational resources. Moreover, these approaches often lack interpretability in decision making. In this study, we propose an HPO based on a Fuzzy Inference System (FIS) approach that integrates a Mamdani FIS into the hyperparameter selection process of a DCNN. It maintains classification accuracy, while speeding up the hyperparameter selection process. The candidate configurations are assessed based on several factors, including validation accuracy, validation loss, and training time, rather than being assessed based on just one factor. These factors are combined into a single, crisp fitness score using fuzzy linguistic rules, providing transparent, interpretable, and multi-objective decision making. In order to minimize computational cost, candidate hyperparameters are first assessed using a quick partial training method that makes use of frozen convolutional layers and warm-started weights. A Mamdani FIS is then used to aggregate performance metrics including accuracy, loss, and training time, producing a crisp score for candidate ranking. After that, training of the chosen hyperparameters is exposed to fully end-to-end training, resulting in superior classification performance with respect to the state-of-the-art exhaustive search techniques. Experiments have been executed on four public plant disease datasets, using an AlexNet-based architecture, where the first experiments have been performed on a baseline model with a randomly-chosen hyperparameter configuration before the fuzzy-guided optimization process is initiated. Then, the model is performed with the best optimized hyperparameter configuration selected by the fuzzy-guided optimization process, demonstrating up to 29% improvement in accuracy, precision, recall and F1-score, along with reliable and comprehensible hyperparameter ranking and optimization. The achieved results show that while keeping an acceptable computing cost, the introduced fuzzy-guided technique consistently outperforms baseline model configurations with respect to all evaluation metrics, yielding greater classification accuracy and enhanced generalization. Hence, our suggested approach provides a lightweight, comprehensible, and efficient solution for hyperparameter tuning in the plant disease classification domain when compared to conventional HPO techniques, offering a practical direction for resource-efficient model optimization in precision agriculture that enables quick deployment of deep learning systems.","深度卷积神经网络（DCNN）在植物病害分类中表现出卓越的性能。然而，其有效性极易受到超参数选择的影响。高效超参数优化（HPO）仍然是将深度学习模型部署于植物病害分类中的一个基本挑战，因为实施网格搜索、贝叶斯优化（BO）和进化算法等大规模搜索技术需要相当大的计算成本。这类传统HPO方法通常依赖单目标适应度标准，并且需要大量计算资源。此外，这些方法在决策过程中往往缺乏可解释性。在本研究中，我们提出了一种基于模糊推理系统（FIS）的HPO方法，将Mamdani FIS集成到DCNN的超参数选择过程中。该方法在保持分类精度的同时，加快了超参数选择过程。候选配置基于多个因素进行评估，包括验证准确率、验证损失和训练时间，而不是仅基于单一因素进行评估。这些因素通过模糊语言规则组合为一个单一的清晰适应度分数，从而提供透明、可解释且多目标的决策。为了最小化计算成本，候选超参数首先通过一种快速部分训练方法进行评估，该方法利用冻结的卷积层和热启动权重。随后，使用Mamdani FIS聚合包括准确率、损失和训练时间在内的性能指标，生成用于候选排序的清晰分数。之后，对所选超参数进行完全端到端训练，从而相对于最先进的穷举搜索技术取得更优的分类性能。实验在四个公开植物病害数据集上执行，采用基于AlexNet的架构，其中在启动模糊引导优化过程之前，首先在随机选择超参数配置的基线模型上进行实验。然后，使用由模糊引导优化过程选出的最佳优化超参数配置运行模型，在准确率、精确率、召回率和F1分数上最高提升29%，同时实现可靠且可理解的超参数排序与优化。所获得的结果表明","Scientific Reports","2026-10-06T00:00:00Z",74,{"impact":282,"substance":182,"depth":101,"authority":283,"freshness":20,"relevant":21,"comment":284},15,14,"提出模糊推理系统驱动的多目标超参数优化方法，在四个公开植物病害数据集上显著提升CNN分类性能，兼具方法新颖性与农业落地价值。",[286],{"name":278,"url":275},[26,27,29,30,288],"超参数优化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":292},{"candidates":293,"agricultural_evidence":45},[294,298],{"code":32,"score":20,"supported":45,"title":295,"abstract":296,"tags":297},[],[48,49],[27,30],{"code":53,"score":299,"supported":45,"title":300,"abstract":301,"tags":302},5,[],[56],[26,29],[304,305],"CNN 植物病害分类","模糊推理 超参数优化","CNN植物病害分类-4425","10.1038\u002Fs41598-026-64880-3",{"doi":307,"openalex_id":309,"authors":310,"venue":278,"cited_by_count":70,"oa_url":326,"card":327,"direction":170,"ingested_from":85},"W7220695608",[311,313,315,318,321,323],{"name":312,"orcid":9},"Medhat Hamdy",{"name":314,"orcid":9},"Heba M. Elhoseny",{"name":316,"orcid":317},"Fathi E. Abd El‐Samie","https:\u002F\u002Forcid.org\u002F0000-0001-8749-9518",{"name":319,"orcid":320},"Adel S. El‐Fishawy","https:\u002F\u002Forcid.org\u002F0000-0003-1567-457X",{"name":322,"orcid":9},"Sami A. El‐Dolil",{"name":324,"orcid":325},"Elhossiny Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0001-5421-2170","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-64880-3_reference.pdf",{"tldr":328,"method":329,"finding":330,"direction":83,"opportunity":331},"提出模糊推理系统引导的CNN超参数优化方法，用于植物病害分类，兼顾精度与效率。","Mamdani模糊推理系统结合部分训练与热启动权重，在四个公开病害数据集上优化A","相比传统穷举搜索，精度等指标最高提升29%，且超参数排序可解释、计算成本更低。","可将模糊多目标HPO迁移到轻量边缘设备或跨作物迁移学习场景，探索动态超参数自适应机制。","2026-10-08T23:30:12.271848Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":341,"sources":343,"tags":345,"primary_topic":347,"secondary_topics":352,"classification":354,"search_phrases":370,"slug":373,"view_count":21,"doi":374,"paper":375,"created_at":390},3885,"InResKolArno_SCO: An Efficient Plant Disease Classification Framework for IoT-Enabled Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.1142\u002Fs0218001426500503","This study proposes a hybrid approach for plant disease classification, integrating Swarm Cray Optimization (SCO) with the InResKolArno, a deep learning model, to address challenges like environmental sensitivity, high computational demands, and limited resources in IoT environments. The process begins with collecting a plant disease recognition dataset, followed by image preprocessing steps such as nearest neighbor interpolation and normalization to improve quality. Disease regions are isolated using color-based thresholding for segmentation. Feature extraction is carried out using deep convolutional neural networks (DCNN), specifically VGG16 and GoogLeNet, to extract high-level leaf features. The InResKolArno classifier, enhanced by SCO (a combination of Particle Swarm Optimization and Crayfish Optimization), is employed to categorize the leaves into three classes: healthy, rust, and powdery mildew. This method improves classification accuracy and reduces data transmission costs in IoT-based precision farming systems. The proposed model achieves high performance with an accuracy of 99.41% and a Positive Predictive Value (PPV) of 99.69%, identifying plant diseases in just 28 seconds. This hybrid model provides superior computational efficiency, making it suitable for real-time deployment in smart farming applications, effectively identifying plant diseases for early intervention and better crop management.","本研究提出了一种植物病害分类的混合方法，将群虾优化算法（Swarm Cray Optimization，SCO）与深度学习模型InResKolArno相结合，以应对物联网环境中的环境敏感性、高计算需求和资源受限等挑战。该流程首先收集植物病害识别数据集，随后进行图像预处理步骤，如最近邻插值和归一化，以提高图像质量。利用基于颜色的阈值分割方法分离病害区域。采用深度卷积神经网络（DCNN），具体为VGG16和GoogLeNet，进行特征提取，以获取叶片的高层特征。经SCO（粒子群优化与螯虾优化的结合）增强的InResKolArno分类器用于将叶片分为三类：健康、锈病和白粉病。该方法提高了分类准确率，并降低了基于物联网的精准农业系统中的数据传输成本。所提出的模型实现了高性能，准确率达99.41%，阳性预测值（PPV）达99.69%，仅需28秒即可识别植物病害。该混合模型具有优越的计算效率，适用于智能农业应用中的实时部署，能够有效识别植物病害，从而实现早期干预和更好的作物管理。","International Journal of Pattern Recognition and Artificial Intelligence","2026-09-30T00:00:00Z",{"impact":99,"substance":100,"depth":101,"authority":102,"freshness":103,"relevant":21,"comment":342},"提出SCO优化InResKolArno的植物病害分类框架，准确率99.41%、28秒识别，面向IoT智慧农业实时部署，方法新颖且数据可靠，值得入选。",[344],{"name":339,"url":336},[26,27,346,29,30],"物联网",{"code":348,"slug":349,"name":350,"path":351,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[353],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":355},{"candidates":356,"agricultural_evidence":45},[357,362,366],{"code":348,"score":282,"supported":45,"title":358,"abstract":360,"tags":361},[359],"IoT",[346,359],[346],{"code":53,"score":102,"supported":45,"title":363,"abstract":364,"tags":365},[122],[29],[26,29],{"code":32,"score":20,"supported":45,"title":367,"abstract":368,"tags":369},[],[48,49],[27,30],[371,372],"InResKolArno SCO 植物病害分类","VGG16 GoogLeNet 病害识别","InResKolArnoSCO植物病害分类-3885","10.1142\u002Fs0218001426500503",{"doi":374,"openalex_id":376,"authors":377,"venue":339,"cited_by_count":70,"oa_url":9,"card":385,"direction":170,"ingested_from":85},"W7214976120",[378,381,383],{"name":379,"orcid":380},"Chitoor Venkat Rao Ajay Kumar","https:\u002F\u002Forcid.org\u002F0009-0001-4082-5416",{"name":382,"orcid":9},"C. Anbuananth",{"name":384,"orcid":9},"Pandi Chiranjeevi",{"tldr":386,"method":387,"finding":388,"direction":170,"opportunity":389},"提出InResKolArno_SCO混合框架，用于物联网智慧农业中的植物病害分类。","VGG16与GoogLeNet提取特征，结合粒子群与螯虾优化算法优化分类器。","在健康、锈病、白粉病三分类上准确率达99.41%，仅需28秒。","可探索轻量化模型在边缘设备上的部署，并扩展至更多病害与田间复杂环境验证。","2026-10-01T23:30:09.723646Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":9,"content":9,"source_name":396,"source_url":394,"published_at":397,"category":12,"cover_url":9,"hotness":398,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":407,"primary_topic":409,"secondary_topics":410,"classification":411,"search_phrases":427,"slug":430,"view_count":299,"doi":431,"paper":432,"created_at":449},3565,"An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability","https:\u002F\u002Fdoi.org\u002F10.7759\u002Fs44389-026-00295-5","An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.","Cureus Journal of Computer Science.","2026-09-25T00:00:00Z",25,39,{"impact":20,"substance":60,"depth":13,"authority":60,"freshness":103,"relevant":21,"comment":401},"主题契合智慧农业与农业AI，但摘要仅重复标题、无方法与数据细节，信息增量有限，暂不建议进入每日精选。",[403,404],{"name":396,"url":394},{"name":405,"url":406},"Cureus Journal of Computer Science 2026-09-25","https:\u002F\u002Fwww.cureusjournals.com\u002Farticles\u002F20543",[26,27,28,29,408],"气候适应",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":412},{"candidates":413,"agricultural_evidence":45},[414,418,422],{"code":53,"score":102,"supported":45,"title":415,"abstract":416,"tags":417},[56],[56],[26,29],{"code":32,"score":102,"supported":45,"title":419,"abstract":420,"tags":421},[50],[50],[27,28],{"code":423,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},"H7",[],[],[408],[428,429],"气候适应 精准农业 可解释AI","农业人工智能 智慧农业 气候适应 精准农业","气候适应精准农业可解释AI-3565","10.7759\u002Fs44389-026-00295-5",{"doi":431,"openalex_id":433,"authors":434,"venue":396,"cited_by_count":70,"oa_url":394,"card":9,"direction":83,"ingested_from":85},"W7214363342",[435,438,441,443,445,447],{"name":436,"orcid":437},"Mritunjay Kr. Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-0240-4909",{"name":439,"orcid":440},"Rohit Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4436-8275",{"name":442,"orcid":9},"Nitin  D Mali",{"name":444,"orcid":9},"Ansh  A Rajore",{"name":446,"orcid":9},"Gaurav Narendra Patil",{"name":448,"orcid":9},"Ankita  N Patil","2026-09-26T23:30:47.893666Z"]