[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3569":3,"related-3569":57},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":56},3569,"Efficient and explainable attention-based multitask learning for fruit type, ripeness, and disease classification","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2108","Precision agriculture increasingly relies on automated crop monitoring to support disease prevention and harvest decisions. However, most existing systems treat fruit type, ripeness, and disease classification as separate tasks, leading to high computational costs in real-world deployments. This paper proposes an efficient and explainable multitask learning (MTL) model for fruit monitoring that performs all three tasks simultaneously. The MTL model uses a shared lightweight MobileNetV2 backbone with a convolutional block attention module (CBAM) to extract both shared and discriminative features. Dedicated classification heads then utilize these features to generate predictions for each task. To address the scarcity of fully annotated agricultural datasets, we combine multiple public datasets and apply a partial label-masking strategy to handle missing annotations. In addition, the proposed model is evaluated using several backbone architectures, including ResNet-50, EfficientNetV2-M, and ViT-B\u002F16, under both single optimizer and separate optimizers. Experimental results demonstrate that the proposed MTL model achieves 99.39% (fruit type), 96.35% (ripeness), and 99.7% (disease) accuracy, while using only 5.5M parameters, outperforming single-task solutions. Furthermore, gradient-based explainability analysis provides transparency into the model's decision-making process.","精准农业日益依赖自动化作物监测，以支持病害预防和收获决策。然而，现有大多数系统将水果类型、成熟度和病害分类视为独立任务，导致实际部署中的计算成本较高。本文提出了一种高效且可解释的多任务学习（MTL）模型用于水果监测，可同时执行上述三项任务。该MTL模型采用共享的轻量级MobileNetV2主干网络，并结合卷积块注意力模块（CBAM）以提取共享特征和判别特征。随后，专用分类头利用这些特征为每个任务生成预测。为应对完全标注农业数据集稀缺的问题，我们整合了多个公开数据集，并采用部分标签掩码策略来处理缺失标注。此外，我们在单一优化器和独立优化器两种设置下，使用包括ResNet-50、EfficientNetV2-M和ViT-B\u002F16在内的多种主干架构对所提模型进行了评估。实验结果表明，所提MTL模型在仅使用5.5M参数的情况下，分别达到了99.39%（水果类型）、96.35%（成熟度）和99.7%（病害）的准确率，优于单任务方案。此外，基于梯度的可解释性分析为模型的决策过程提供了透明度。",null,"Journal of Agricultural Engineering","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出轻量可解释多任务模型，同时识别水果种类、成熟度与病害，精度高、参数少，对智慧果园部署有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","病害识别","多任务学习","水果检测",[32,33],"MobileNetV2 CBAM 水果分类","多任务学习 果实成熟度 病害","MobileNetV2CBAM水果分类-3569",0,"10.4081\u002Fjae.2026.2108",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":49,"direction":53,"ingested_from":55},"W7214312987",[40,43,46],{"name":41,"orcid":42},"Rida El Chall","https:\u002F\u002Forcid.org\u002F0000-0002-7620-7767",{"name":44,"orcid":45},"Sarah Alayan","https:\u002F\u002Forcid.org\u002F0009-0007-5706-4688",{"name":47,"orcid":48},"Abed Ellatif Samhat","https:\u002F\u002Forcid.org\u002F0000-0002-1137-621X",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"提出轻量多任务模型，同时分类水果种类、成熟度和病害，并具可解释性。","MobileNetV2+CBAM共享主干，多数据集合并与部分标签掩码。","三任务准确率达99.39%、96.35%、99.7%，仅5.5M参数，优于单任务。","农业人工智能与决策模型","可探索多任务模型在田间实时部署与跨作物泛化，结合弱监督降低标注成本。","openalex","2026-09-26T23:30:57.555191Z",{"total":58,"page":21,"page_size":58,"items":59},6,[60,98,139,168,211,259],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":9,"source_name":66,"source_url":63,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":74,"tags":76,"search_phrases":79,"slug":82,"view_count":35,"doi":83,"paper":84,"created_at":97},3517,"A Resource-Efficient Hybrid CNN-LSTM Network for Image-Based Bean Leaf Disease Classification","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjimaging12100468","Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on portable devices. This paper proposes a lightweight hybrid CNN-LSTM system for bean leaf disease classification. By integrating an LSTM layer to model the spatial–sequential relationships within feature maps, our hybrid architecture achieves a 94.36% accuracy and 94.38% F1 score while maintaining an exceptionally small footprint of 1.86 MB, a 70% reduction in size compared to traditional CNN-based systems. Furthermore, we provide a systematic evaluation of image augmentation strategies, demonstrating that tailored transformations are superior to generic combinations for maintaining the integrity of diagnostic patterns. Results on the ibean dataset confirm that the proposed system achieves state-of-the-art F1 scores of 99.22% with EfficientNet-B7+LSTM, providing a potentially robust and scalable framework for real-time agricultural decision support in resource-constrained environments. The code and augmented datasets used in this study are publicly available on this GitHub repo.","准确且资源高效的自动化诊断是现代农业专家系统的基石。尽管卷积神经网络（CNN）在植物病理学领域已确立了基准，但其捕捉长程空间依赖关系的能力常受限于标准池化层，且高内存占用阻碍了其在便携设备上的部署。本文提出了一种用于豆叶病害分类的轻量级混合CNN-LSTM系统。通过集成LSTM层来建模特征图内的空间-序列关系，我们的混合架构达到了94.36%的准确率和94.38%的F1分数，同时保持了仅1.86 MB的极小占用，相较于传统基于CNN的系统体积减少了70%。此外，我们系统评估了图像增强策略，表明定制化变换在保持诊断模式完整性方面优于通用组合。在ibean数据集上的结果证实，所提出的系统结合EfficientNet-B7+LSTM达到了99.22%的最先进F1分数，为资源受限环境中的实时农业决策支持提供了一个潜在稳健且可扩展的框架。本研究使用的代码和增强数据集已在此GitHub仓库公开。","Journal of Imaging","2026-09-24T00:00:00Z",78,{"impact":70,"substance":18,"depth":17,"authority":71,"freshness":72,"relevant":21,"comment":73},16,13,9,"轻量级CNN-LSTM豆叶病害识别研究，方法新颖、数据可靠且代码开源，对资源受限场景下的农业智能诊断有实用价值。",[75],{"name":66,"url":63},[26,27,28,77,78],"轻量化模型","豆类作物",[80,81],"ibean dataset 豆叶病害","农业人工智能 轻量化模型 智慧农业 病害识别","ibeandataset豆叶病害-3517","10.3390\u002Fjimaging12100468",{"doi":83,"openalex_id":85,"authors":86,"venue":66,"cited_by_count":35,"oa_url":63,"card":92,"direction":53,"ingested_from":55},"W7154572440",[87,89],{"name":88,"orcid":9},"Hye Jin Rhee",{"name":90,"orcid":91},"Joseph Damilola Akinyemi","https:\u002F\u002Forcid.org\u002F0000-0003-3121-4231",{"tldr":93,"method":94,"finding":95,"direction":53,"opportunity":96},"提出轻量级CNN-LSTM混合网络，用于豆叶病害分类，兼顾高精度与低资源占用。","CNN提取特征后接LSTM建模空间序列关系，在ibean数据集上评估并系统比较图","模型准确率94.36%、F1 94.38%，仅1.86MB，比传统CNN缩小70%，Efficien","可探索面向移动端\u002F边缘设备的超轻量病害诊断模型，并研究增强策略与模型结构的自适应协同优化。","2026-09-25T23:30:59.276381Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":35,"doi":118,"paper":119,"created_at":138},3368,"A PCA-based deep feature optimization framework for explainable orange fruit disease classification","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09984-8","Accurate classification of orange fruit diseases is important for precision agriculture and yield protection. This study develops and rigorously benchmarks a hybrid deep-feature framework for classifying Black Spot, Canker, Fresh, and Greening oranges (1,090 images), combining deep feature extraction, PCA-based dimensionality reduction, and classical machine-learning classification. Eight backbones (seven CNNs and a Vision Transformer, ViT) and four classifiers (32 configurations in total) were evaluated under 5 × 5 repeated stratified cross-validation, with PCA fitted exclusively on training-fold features in every iteration to eliminate data leakage. The proposed ViT + PCA+SVM configuration achieved the highest mean accuracy, 99.12%±0.71%, significantly outperforming every CNN-based backbone, including DenseNet201 + PCA + SVM (98.48%±0.81%, p \u003C 0.001). A dedicated variance-retention sensitivity analysis justifies the 98% threshold used throughout, and ablation experiments confirm that PCA substantially reduces feature dimensionality (by ~ 55.7% for ViT and ~ 76.6% for DenseNet201) without a significant loss of accuracy for either backbone. Explainability analysis — occlusion sensitivity and SHAP for the proposed ViT model, and Grad-CAM and SHAP for the DenseNet201 comparison model — shows that both configurations base predictions on biologically relevant, disease-affected regions of the fruit rather than spurious cues. These results identify ViT + PCA+SVM as the most accurate configuration evaluated, with DenseNet201 + PCA + SVM as a closely competitive, more compact convolutional alternative for intelligent orchard disease-monitoring systems.","橙类果实病害的准确分类对精准农业和产量保护具有重要意义。本研究开发并严格基准测试了一种混合深度特征框架，用于对黑斑病、溃疡病、新鲜和黄龙病橙类（1，090张图像）进行分类，该框架结合了深度特征提取、基于PCA的降维和经典机器学习分类。在5×5重复分层交叉验证下评估了八种骨干网络（七种CNN和一种视觉Transformer，ViT）和四种分类器（共32种配置），每次迭代中PCA仅在训练折特征上拟合以消除数据泄漏。所提出的ViT + PCA+SVM配置取得了最高平均准确率，为99.12%±0.71%，显著优于所有基于CNN的骨干网络，包括DenseNet201 + PCA + SVM（98.48%±0.81%，p \u003C 0.001）。专门的方差保留敏感性分析证明了全程使用的98%阈值是合理的，消融实验证实PCA大幅降低了特征维度（ViT约降低55.7%，DenseNet201约降低76.6%），且两种骨干网络均无显著准确率损失。可解释性分析——对所提出的ViT模型采用遮挡敏感性和SHAP，对DenseNet201对比模型采用Grad-CAM和SHAP——表明两种配置均基于果实中生物学相关的病害影响区域而非虚假线索进行预测。这些结果确定ViT + PCA+SVM为所评估的最准确配置，而DenseNet201 + PCA + SVM则是一种竞争力接近且更紧凑的卷积替代方案，可用于智能果园病害监测系统。","BMC Plant Biology","2026-09-23T00:00:00Z",79,{"impact":70,"substance":18,"depth":17,"authority":19,"freshness":72,"relevant":21,"comment":108},"方法严谨、数据与消融实验充分，对果园智能病害监测有实用参考价值，但属细分技术论文，产业影响有限。",[110],{"name":104,"url":101},[26,27,112,28,113],"可解释AI","柑橘种植",[115,116],"柑橘病害 深度学习 分类","ViT PCA SVM 病害识别","柑橘病害深度学习分类-3368","10.1186\u002Fs12870-026-09984-8",{"doi":118,"openalex_id":120,"authors":121,"venue":104,"cited_by_count":35,"oa_url":101,"card":133,"direction":53,"ingested_from":55},"W7214068709",[122,124,126,128,131],{"name":123,"orcid":9},"Amruta Hingmire",{"name":125,"orcid":9},"Avinash Golande",{"name":127,"orcid":9},"Vinodkumar Bhutnal",{"name":129,"orcid":130},"Sagar Dhanraj Pande","https:\u002F\u002Forcid.org\u002F0000-0003-4506-6997",{"name":132,"orcid":9},"Tanuja Pande",{"tldr":134,"method":135,"finding":136,"direction":53,"opportunity":137},"提出PCA深度特征优化框架，用ViT+SVM分类橙子病害，准确率达99.12%。","8种骨干网络提取特征，PCA降维，4种分类器，5×5交叉验证。","ViT+PCA+SVM最优，PCA降维超55%且精度不降，可解释性验证有效。","可探索轻量化模型在移动端或边缘设备的实时病害检测与多作物泛化。","2026-09-24T23:30:34.116938Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":9,"content":9,"source_name":144,"source_url":9,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":149,"tags":151,"search_phrases":154,"slug":157,"view_count":35,"doi":9,"paper":158,"created_at":167},3323,"基于空间分辨光谱的鲜食玉米含水率和硬度MaMoNet检测模型——融合Mamba状态空间模型与多门专家混合机制","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5953466437_162dab0450670bdama.html","《智慧农业（中英文）》2026年第4期。许敏、赵鑫、陈艳萍、朱启兵、黄敏（江南大学\u002F江苏省农科院）以带苞叶鲜食玉米为研究对象，构建多通道可见光-近红外空间分辨光谱采集系统，获取玉米样本的多通道光谱数据，并提出一种融合Mamba状态空间模型与多门专家混合(MMoE)机制的多任务预测网络——MaMoNet(Mamba-MMoE Network)。MaMoNet在测试集上玉米籽粒含水率预测的决定系数R²达到了0.91，硬度预测R²达到了0.89，均优于对比模型。消融实验进一步证明Mamba模块在建模光谱长程依赖关系方面具有显著优势，以及MMoE机制能够有效缓解多任务学习中任务间特征竞争问题。","《智慧农业（中英文）》2026年第4期","2026-09-20T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":72,"relevant":21,"comment":148},"核心期刊论文，方法新颖且指标可靠，对农产品无损检测有实质参考价值。",[150],{"name":144,"url":142},[26,27,152,153,29],"鲜食玉米","光谱检测",[155,156],"江南大学 鲜食玉米 含水率","MaMoNet 玉米 硬度","江南大学鲜食玉米含水率-3323",{"doi":9,"openalex_id":9,"authors":159,"venue":9,"cited_by_count":35,"oa_url":9,"card":160,"direction":164,"ingested_from":166},[],{"tldr":161,"method":162,"finding":163,"direction":164,"opportunity":165},"提出MaMoNet网络，用空间分辨光谱检测鲜食玉米含水率和硬度。","多通道可见光-近红外空间分辨光谱，融合Mamba与MMoE多任务网络。","含水率R²达0.91、硬度R²达0.89，优于对比模型，Mamba与MMoE均有效。","农业遥感与作物表型","可探索Mamba在多作物多品质指标无损检测中的泛化性及田间在线部署。","agent","2026-09-24T00:04:02.557420Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":182,"tags":184,"search_phrases":187,"slug":190,"view_count":35,"doi":191,"paper":192,"created_at":210},3256,"Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112464","Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification。Computers and Electronics in Agriculture","面向稳健杏仁病害分类的泄漏感知、校准且可解释的深度学习。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-22T00:00:00Z",72,{"impact":178,"substance":179,"depth":180,"authority":19,"freshness":72,"relevant":21,"comment":181},12,20,17,"核心期刊论文，方法上有防泄漏、校准与可解释性创新，但作物小众、属细分技术进展，未达每日精选门槛。",[183],{"name":174,"url":171},[26,27,185,28,186],"深度学习","巴旦木",[188,189],"巴旦木 病害 深度学习","农业人工智能 智慧农业 深度学习 病害识别","巴旦木病害深度学习-3256","10.1016\u002Fj.compag.2026.112464",{"doi":191,"openalex_id":193,"authors":194,"venue":174,"cited_by_count":35,"oa_url":171,"card":205,"direction":53,"ingested_from":55},"W7213988471",[195,198,201,203],{"name":196,"orcid":197},"Abebaw Degu Workneh","https:\u002F\u002Forcid.org\u002F0000-0001-7694-1577",{"name":199,"orcid":200},"Badr Elkari","https:\u002F\u002Forcid.org\u002F0000-0002-0893-783X",{"name":202,"orcid":9},"Meryam El Mouhtadi",{"name":204,"orcid":9},"Mohammad Furqan Ali",{"tldr":206,"method":207,"finding":208,"direction":53,"opportunity":209},"提出防泄漏、校准且可解释的深度学习框架，用于稳健的杏仁病害分类。","采用防数据泄漏的深度学习训练、概率校准与可解释性分析。","该框架能提升杏仁病害分类的稳健性、可信度与可解释性。","可探索防泄漏与校准机制在其他作物病害识别中的泛化及田间部署。","2026-09-23T23:30:01.628054Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"search_phrases":226,"slug":229,"view_count":35,"doi":230,"paper":231,"created_at":258},3153,"Explainable optimized deep learning and generative AI based framework for finger millet disease detection in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02222-y","Abstract Eleusine coracana, locally known as finger millet (ragi), is a wholesome, climate-resilient crop. It is a staple food in semi-dry and dry areas of Asia and Africa. The productivity of this crop can be severely reduced by diseases such as downy, mottle, seedling, smut, and wilt. Conventional diagnosis methods are often time-consuming and unsuitable for large-scale farming. Therefore,Deep Learning (DL) offers an alternative for smart commercial farming that can streamline disease screening and improve productivity. This work offers an AI-integrated framework for early disease intervention combining DL, Explainable AI (XAI), and an image-based decision-support interface. The finger millet (ragi) dataset from Kaggle was used to train and evaluate custom CNN, VGG16, and ResNet50 models. To reduce feature redundancy and improve generalization, Grey Wolf Optimizer (GWO) based feature selection was applied to the penultimate-layer features of each model. Custom CNN, VGG16, and ResNet50 achieved accuracies of 89.40%, 83.11%, and 91.83%, respectively. Further, the use of GWO improved the accuracies to 97.71%, 90.84%, and 98.36%, respectively. ResNet50 achieved the highest accuracy 98.36% and was selected as the final backbone. The final ResNet50 model is integrated with the Gemini API to provide disease-specific recommendations based on the predicted class and user queries. To increase prediction transparency, Grad-CAM was used to highlight image regions that influenced the model output. The integration of DL, XAI, and generative AI provides a scalable approach for detecting and managing finger millet diseases. The proposed work boosts precision agriculture by providing AI-based and XAI-supported decision-making, while fostering sustainable agricultural practices.","摘要 穇子（Eleusine coracana），当地称为指黍（ragi），是一种有益健康且气候适应性强的作物。它是亚洲和非洲半干旱及干旱地区的主食。霜霉病、斑驳病、苗枯病、黑穗病和枯萎病等病害可严重降低该作物的产量。传统诊断方法往往耗时且不适合大规模种植。因此，深度学习（DL）为智能商业农业提供了一种替代方案，可简化病害筛查并提高生产力。本研究提出了一种人工智能集成框架，用于早期病害干预，结合了深度学习、可解释人工智能（XAI）和基于图像的决策支持界面。使用来自Kaggle的指黍（ragi）数据集训练和评估了自定义CNN、VGG16和ResNet50模型。为减少特征冗余并提高泛化能力，将灰狼优化器（GWO）基于特征选择应用于每个模型的倒数第二层特征。自定义CNN、VGG16和ResNet50分别达到了89.40%、83.11%和91.83%的准确率。此外，使用GWO将准确率分别提高到97.71%、90.84%和98.36%。ResNet50达到了最高准确率98.36%，并被选为最终骨干网络。最终的ResNet50模型与Gemini API集成，根据预测类别和用户查询提供针对特定病害的建议。为提高预测透明度，使用Grad-CAM突出显示影响模型输出的图像区域。深度学习、可解释人工智能和生成式人工智能的集成提供了一种可扩展的方法，用于检测和管理指黍病害。所提出的工作通过提供基于人工智能和可解释人工智能支持的决策，促进了精准农业，同时推动了可持续农业实践。","Discover Artificial Intelligence",75,{"impact":220,"substance":179,"depth":17,"authority":178,"freshness":13,"relevant":21,"comment":221},15,"将深度学习、可解释AI与生成式AI结合用于指状粟病害识别，方法新颖、数据充分，对智慧农业有参考价值。",[223],{"name":217,"url":214},[26,27,112,28,225],"小米作物",[227,228],"ResNet50 GWO 病害识别","农业人工智能 小米作物 智慧农业 病害识别","ResNet50GWO病害识别-3153","10.1007\u002Fs44163-026-02222-y",{"doi":230,"openalex_id":232,"authors":233,"venue":217,"cited_by_count":35,"oa_url":214,"card":252,"direction":257,"ingested_from":55},"W7213978567",[234,237,239,241,243,246,249],{"name":235,"orcid":236},"Sunil Kumar Mohapatra","https:\u002F\u002Forcid.org\u002F0000-0002-5865-095X",{"name":238,"orcid":9},"A. Sanjib Kumar Patro",{"name":240,"orcid":9},"Lulen Kumar Sahu",{"name":242,"orcid":9},"Chinmaye Dora",{"name":244,"orcid":245},"Sujata Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-1293-5378",{"name":247,"orcid":248},"Kshira Sagar Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-6435-5738",{"name":250,"orcid":251},"Byomakesh Mahapatra","https:\u002F\u002Forcid.org\u002F0000-0002-9126-1729",{"tldr":253,"method":254,"finding":255,"direction":53,"opportunity":256},"提出融合深度学习、可解释AI与生成式AI的指状粟病害检测框架，实现高精度识别与决策支持。","用Kaggle指状粟图像训练CNN、VGG16、ResNet50，并用灰狼优化做","GWO特征选择显著提升精度，ResNet50达98.36%，结合Grad-CAM与Gemini AP","可探索轻量化模型与多作物泛化，并将XAI与生成式建议在田间移动端实时验证。","智慧农业 \u002F 农业物联网","2026-09-22T23:30:10.255790Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":267,"sources":269,"tags":271,"search_phrases":273,"slug":276,"view_count":21,"doi":277,"paper":278,"created_at":296},2910,"Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10451-5","Abstract Purpose Throughout the growing season, leaf diseases pose a huge risk to apple fruit yield and quality. While early detection targets timely intervention, understanding disease progression across different stages is essential for effective treatment and resistance breeding. Methods This study proposes a deep learning-based detection system that identifies apple leaf diseases at various infection stages in real-world environments. Most existing methods have focused on disease detection in controlled conditions, while the developed system operates in complex field settings with variable lighting and background noise. Two YOLOv8-based variants integrated with a multi-head self-attention (MHSA) module were developed to improve detection of dense, tiny, and feature-similar lesions. In addition, a web-based interactive tool was built by combining the Segment Anything Model (SAM) with the detection model to support leaf isolation, disease localization, infection estimation, severity categorization, and automatic report generation. Results The training results on proposed PA-ALeaf dataset demonstrated that YOLOv8-MHSA_b_h varaint achieved the highest performance (Precision 60.5%, mAP50 56.3%, F1 confidence 56.0%), while maintaining real-time inference (12.38 ms per image), compared to state-of-the-art models. Conclusion By bridging practical disease monitoring and scientific research, our system offers a comprehensive, scalable solution for apple leaf disease detection in real-world orchards. This system could benefit both farmers by enabling multi-stage intervention and researchers by providing insights into disease progression.","摘要 目的 在整个生长季中，叶片病害对苹果果实产量和品质构成巨大风险。虽然早期检测有助于及时干预，但了解病害在不同阶段的进展对于有效治疗和抗性育种至关重要。方法 本研究提出了一种基于深度学习的检测系统，可在真实环境中识别不同感染阶段的苹果叶片病害。现有大多数方法侧重于受控条件下的病害检测，而所开发的系统可在光照变化和背景噪声复杂的田间环境中运行。研究开发了两种基于YOLOv8的变体，并集成了多头自注意力（MHSA）模块，以提高对密集、微小和特征相似病斑的检测能力。此外，通过将分割一切模型（SAM）与检测模型相结合，构建了一个基于网络的交互式工具，以支持叶片分离、病害定位、感染估计、严重程度分类和自动报告生成。结果 在所提出的PA-ALeaf数据集上的训练结果表明，与最先进的模型相比，YOLOv8-MHSA_b_h变体取得了最高性能（精确率60.5%，mAP50 56.3%，F1置信度56.0%），同时保持实时推理（每张图像12.38 ms）。结论 通过连接实际病害监测与科学研究，我们的系统为真实果园中的苹果叶片病害检测提供了一种全面、可扩展的解决方案。该系统既可使农民受益，实现多阶段干预，也可为研究人员提供病害进展的见解。","Precision Agriculture","2026-09-18T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":268},"提出面向真实果园的多阶段苹果叶病检测系统并配套网页工具，方法新颖、数据与性能指标明确，对智慧植保具有参考价值。",[270],{"name":265,"url":262},[26,27,185,28,272],"苹果病害",[274,275],"苹果叶部病害 深度学习 识别","YOLOv8 苹果病害 检测","苹果叶部病害深度学习识别-2910","10.1007\u002Fs11119-026-10451-5",{"doi":277,"openalex_id":279,"authors":280,"venue":265,"cited_by_count":35,"oa_url":262,"card":291,"direction":53,"ingested_from":55},"W7213537759",[281,283,286,288],{"name":282,"orcid":9},"Kangrui Han",{"name":284,"orcid":285},"Hao Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9879-2960",{"name":287,"orcid":9},"Kari Peter",{"name":289,"orcid":290},"Long He","https:\u002F\u002Forcid.org\u002F0000-0001-9781-6062",{"tldr":292,"method":293,"finding":294,"direction":53,"opportunity":295},"提出基于YOLOv8-MHSA与SAM的网页系统，实现苹果叶片病害多感染阶段实时检测与分级。","YOLOv8+多头自注意力，结合SAM分割，构建PA-ALeaf数据集与网页工具","YOLOv8-MHSA_b_h变体精度60.5%、mAP50 56.3%，单图推理12.38ms，可","可探索轻量化模型在移动端的部署，并融合时序数据预测病害发展轨迹。","2026-09-19T23:30:03.474350Z"]