[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2910":3,"related-2910":58},{"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":57},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。",null,"Precision Agriculture","2026-09-18T00:00:00Z","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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","深度学习","病害识别","苹果病害",[32,33],"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",0,"10.1007\u002Fs11119-026-10451-5",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7213537759",[40,42,45,47],{"name":41,"orcid":9},"Kangrui Han",{"name":43,"orcid":44},"Hao Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9879-2960",{"name":46,"orcid":9},"Kari Peter",{"name":48,"orcid":49},"Long He","https:\u002F\u002Forcid.org\u002F0000-0001-9781-6062",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"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","农业人工智能与决策模型","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。","openalex","2026-09-19T23:30:03.474350Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,110,149,182,216,244],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":75,"tags":77,"search_phrases":79,"slug":82,"view_count":35,"doi":83,"paper":84,"created_at":109},1623,"Deep convolutional neural network-based automated identification and classification of mungbean foliar diseases","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1848787","Early and accurate detection of plant diseases is critical in precision agriculture to improve crop management and yield. Mungbean ( Vigna radiata L.) is highly susceptible to several foliar diseases, including yellow mosaic, powdery mildew, leaf crinkle, and cercospora leaf spot, which cause substantial productivity losses. Despite expanding applications of deep learning in plant disease diagnosis, systematic multi-architecture evaluation for mungbean disease classification under natural field conditions remains limited. This study addresses this gap by evaluating five state-of-the-art deep convolutional neural network (DCNN) architectures on a large-scale, field-acquired mungbean dataset that captures real-world variability across environmental conditions and disease severity levels, distinguishing it from controlled laboratory studies. A total of 5,617 original images across five classes were used. Data augmentation was applied exclusively to the training subset after stratified splitting to prevent data leakage. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets. VGG16, VGG19, ResNet50V2, DenseNet121, and InceptionV3 were evaluated using identical transfer learning and fine-tuning protocols. Model performance was assessed using AUC-ROC, Cohen's kappa coefficient, McNemar's test for pairwise statistical comparisons, five-fold cross-validation, and Grad-CAM-based interpretability. On the independent test set, InceptionV3 achieved the highest accuracy (98.47%) and macro-F1 (98.49%), followed by VGG16 (98.36%) and VGG19 (97.89%). AUC-ROC values exceeded 0.997 for all models, confirming excellent class discrimination. Grad-CAM visualizations further confirmed that model predictions were based on biologically relevant disease symptoms. The findings demonstrate the effectiveness of deep learning for robust disease recognition under realistic field conditions and highlight the potential of AI-based diagnostic tools for crop health monitoring, precision agriculture, and decision-support systems in mungbean production.","植物病害的早期准确检测对于精准农业中改善作物管理和提高产量至关重要。绿豆（*Vigna radiata* L.）极易感染多种叶部病害，包括黄花叶病、白粉病、叶皱缩病和尾孢叶斑病，这些病害会导致显著的生产力损失。尽管深度学习在植物病害诊断中的应用日益广泛，但在自然田间条件下对绿豆病害分类进行系统性多架构评估的研究仍然有限。本研究通过在大规模、田间采集的绿豆数据集上评估五种最先进的深度卷积神经网络（DCNN）架构来填补这一空白，该数据集捕捉了环境条件和病害严重程度方面的真实世界变异性，使其区别于受控实验室研究。共使用了涵盖五个类别的5,617张原始图像。在分层划分后，数据增强仅应用于训练子集，以防止数据泄漏。数据集被划分为训练集（70%）、验证集（15%）和测试集（15%）。VGG16、VGG19、ResNet50V2、DenseNet121和InceptionV3采用相同的迁移学习和微调协议进行评估。模型性能通过AUC-ROC、Cohen's kappa系数、用于成对统计比较的McNemar检验、五折交叉验证以及基于Grad-CAM的可解释性进行评估。在独立测试集上，InceptionV3取得了最高的准确率（98.47%）和宏平均F1分数（98.49%），其次是VGG16（98.36%）和VGG19（97.89%）。所有模型的AUC-ROC值均超过0.997，证实了优异的类别区分能力。Grad-CAM可视化进一步证实了模型预测基于生物学相关的病害症状。研究结果表明，深度学习在现实田间条件下实现稳健病害识别方面具有有效性，并凸显了基于人工智能的诊断工具在绿豆生产中的作物健康监测、精准农业和决策支持系统方面的应用潜力。","Frontiers in Artificial Intelligence","2026-09-03T00:00:00Z",74,{"impact":71,"substance":18,"depth":17,"authority":72,"freshness":73,"relevant":21,"comment":74},15,12,7,"系统评估多种深度学习模型在绿豆叶部病害识别中的性能，数据规模大且基于田间条件，具有较强应用价值。",[76],{"name":67,"url":64},[26,27,78,28,29],"绿豆",[80,81],"农业人工智能 智慧农业 深度学习 病害识别","农业人工智能 智慧农业","农业人工智能智慧农业深度学习病害识别-1623","10.3389\u002Ffrai.2026.1848787",{"doi":83,"openalex_id":85,"authors":86,"venue":67,"cited_by_count":35,"oa_url":102,"card":103,"direction":108,"ingested_from":56},"W7207845216",[87,89,91,94,96,98,100],{"name":88,"orcid":9},"Shail Bala",{"name":90,"orcid":9},"SI Harlapur",{"name":92,"orcid":93},"Aditya Kamalakar Kanade","https:\u002F\u002Forcid.org\u002F0009-0004-8685-3326",{"name":95,"orcid":9},"M. P. Potdar",{"name":97,"orcid":9},"Gurupada Balol",{"name":99,"orcid":9},"VB Kuligod",{"name":101,"orcid":9},"Lingareddy Usha Rani","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1848787\u002Fpdf",{"tldr":104,"method":105,"finding":106,"direction":54,"opportunity":107},"评估五种深度卷积神经网络在自然田间条件下对绿豆叶部病害的识别性能。","使用5617张田间图像，五种DCNN架构，迁移学习与微调，数据增强，Grad-C","InceptionV3准确率最高（98.47%），所有模型AUC>0.997，Grad-CAM显示基","可扩展至其他作物多病害识别，或开发轻量级模型用于移动端实时诊断，结合无人机图像实现大范围监测。","智慧农业 \u002F 农业物联网","2026-09-04T23:30:11.523875Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"search_phrases":127,"slug":130,"view_count":35,"doi":131,"paper":132,"created_at":148},2963,"A comprehensive review of deep learning methods for weed classification in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-01916-7","Weeds today are among the factors contributing to low agricultural productivity. As the world’s population continues to grow, there is an urgent need to meet global food demand. Nigeria currently lacks sufficient crop production to feed its growing population, and weeds are among the core contributors to poor agricultural yield. This study conducts a comprehensive review of Deep Learning (DL) approaches to weed classification in precision agriculture, covering literature from 2018 to 2025, was carried out. We employed a mix of quantitative and qualitative methods in the course of this review paper. Our data source is centred on Scopus-indexed papers, published with Sensors, Electronics, and Agriculture in MDPI as well as IEEE, Thomson Reuters, and Springer. The study systematically reviewed and analysed machine learning (ML), DL, and instance segmentation techniques to identify the key technological and environmental barriers, such as data limitations, class imbalance, environmental variability, and model scalability issues that affect the effectiveness and efficiency of these models when deployed in real time. These findings show that while weed management models like the YOLO variants, ResNet, and Vision Transformers achieved high accuracy in training and testing, they are associated with several challenges in their real world-deployment, such as occlusion, small object detection, and environmental adaptability. Overall, this research provides recommended solutions to enhance model robustness, scalability, and efficiency. It further provides a summary of the current state and future directions for AI-driven weed management.","杂草是当前导致农业生产力低下的因素之一。随着世界人口持续增长，满足全球粮食需求已成为迫切任务。尼日利亚目前的作物产量不足以养活其不断增长的人口，而杂草是导致农业产量低下的核心因素之一。本研究对精准农业中基于深度学习（Deep Learning，DL）的杂草分类方法进行了全面综述，涵盖2018年至2025年的文献。在综述过程中，我们采用了定量与定性相结合的方法。数据来源集中于Scopus索引论文，这些论文发表于MDPI旗下的Sensors、Electronics和Agriculture，以及IEEE、Thomson Reuters和Springer。本研究系统综述并分析了机器学习（Machine Learning，ML）、深度学习及实例分割技术，以识别影响这些模型实时部署效果与效率的关键技术和环境障碍，如数据局限性、类别不平衡、环境变异性及模型可扩展性问题。研究结果表明，尽管YOLO系列、ResNet和视觉Transformer（Vision Transformer）等杂草管理模型在训练和测试中达到了较高精度，但在实际部署中仍面临诸多挑战，如遮挡、小目标检测和环境适应性等问题。总体而言，本研究提出了增强模型鲁棒性、可扩展性和效率的推荐解决方案，并进一步总结了人工智能驱动杂草管理的现状与未来方向。","Discover Artificial Intelligence",67,{"impact":72,"substance":17,"depth":119,"authority":120,"freshness":20,"relevant":21,"comment":121},16,13,"系统综述2018—2025年深度学习杂草分类方法，指出遮挡、小目标与环境适应性等落地瓶颈，对农业AI研究有参考价值，但属综述类论文、非突破性成果。",[123],{"name":116,"url":113},[26,27,28,125,126],"杂草识别","精准农业",[128,129],"深度学习 杂草分类 精准农业","YOLO 杂草识别 模型部署","深度学习杂草分类精准农业-2963","10.1007\u002Fs44163-026-01916-7",{"doi":131,"openalex_id":133,"authors":134,"venue":116,"cited_by_count":35,"oa_url":113,"card":143,"direction":54,"ingested_from":56},"W7213558223",[135,137,139,141],{"name":136,"orcid":9},"Njoku Camillus Ekene",{"name":138,"orcid":9},"Francis A. Okoye",{"name":140,"orcid":9},"Ebere Uzoka Chidi",{"name":142,"orcid":9},"OGBU MARY NNENNA",{"tldr":144,"method":145,"finding":146,"direction":54,"opportunity":147},"综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。","混合定量定性法，基于Scopus及MDPI、IEEE等文献，分析ML、DL与实例","YOLO、ResNet、ViT等精度高，但实际部署受遮挡、小目标与环境适应性限制。","可研究轻量化、跨域自适应模型，解决小目标与遮挡下的实时杂草识别难题。","2026-09-19T23:30:56.875661Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":162,"tags":164,"search_phrases":166,"slug":169,"view_count":35,"doi":170,"paper":171,"created_at":181},2940,"A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection","https:\u002F\u002Fdoi.org\u002F10.53365\u002Fnrfhh.1816","Cotton production is frequently affected by leaf diseases that can reduce plant productivity, deteriorate crop quality, and cause considerable financial losses for farmers. Consequently, rapid and reliable disease identification is an important requirement for precision agriculture and effective crop protection. Conventional image-based approaches predominantly employ CNN architectures for visual classification; however, such methods may have limited capability in learning long-range spatial dependencies and modeling changes associated with disease development over time. To overcome these limitations, this research introduces an integrated hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Transformer-based self-attention, and Long Short-Term Memory (LSTM) networks for intelligent cotton leaf disease recognition. The proposed framework utilizes a pre-trained EfficientNet network to extract discriminative spatial characteristics from cotton leaf images. The extracted representations are subsequently processed through a multi-head self-attention Transformer module, which enables the network to identify relationships between distant and relevant regions of the leaf. An LSTM component is then incorporated to learn sequential dependencies and provide a foundation for analyzing disease evolution and progression. To improve model reliability and generalization, the network is trained using an appropriately organized cotton leaf image dataset together with image augmentation, transfer learning, and fine-tuning techniques. The experimental evaluation indicates that the proposed CNN–Transformer–LSTM architecture provides improved disease classification performance and more effective feature learning compared with conventional CNN-based models. Performance assessment using classification metrics, confusion matrices, and ROC curves demonstrates strong discrimination among the considered cotton disease categories. The combined learning of local visual characteristics, global contextual information, and sequential dependencies provides a comprehensive framework for automated cotton disease assessment. The proposed approach can serve as a scalable artificial intelligence solution for precision agriculture applications. Its architecture also provides opportunities for future integration with IoT-based crop monitoring systems, environmental data acquisition, and disease progression prediction, supporting early warning systems and intelligent crop health management in smart farming environments.","棉花生产常受叶片病害影响，这些病害会降低植株生产力、恶化作物品质，并给农民造成相当大的经济损失。因此，快速可靠的病害识别是精准农业和有效作物保护的重要需求。传统的基于图像的方法主要采用CNN架构进行视觉分类；然而，此类方法在学习长程空间依赖关系以及建模与病害随时间发展相关的变化方面能力有限。为克服这些局限，本研究提出了一种集成混合深度学习框架，将卷积神经网络（CNN）、基于Transformer的自注意力机制和长短期记忆（LSTM）网络相结合，用于智能棉花叶片病害识别。所提出的框架利用预训练的EfficientNet网络从棉花叶片图像中提取具有判别力的空间特征。提取到的表示随后通过多头自注意力Transformer模块进行处理，使网络能够识别叶片中相距较远且相关区域之间的关系。随后引入LSTM组件以学习序列依赖关系，并为分析病害演变和进展提供基础。为提高模型可靠性和泛化能力，网络使用组织良好的棉花叶片图像数据集进行训练，并结合图像增强、迁移学习和微调技术。实验评估表明，与传统的基于CNN的模型相比，所提出的CNN–Transformer–LSTM架构提供了更好的病害分类性能和更有效的特征学习。使用分类指标、混淆矩阵和ROC曲线进行的性能评估表明，该方法在所考虑的棉花病害类别之间具有较强的判别能力。局部视觉特征、全局上下文信息和序列依赖关系的联合学习为自动化棉花病害评估提供了一个全面的框架。所提出的方法可作为精准农业应用中可扩展的人工智能解决方案。其架构也为未来与基于物联网的作物监测系统、环境数据采集和病害进展预测的集成提供了机会，从而支持预警系统和智能","Natural Resources for Human Health","2026-09-16T00:00:00Z",69,{"impact":72,"substance":159,"depth":160,"authority":72,"freshness":20,"relevant":21,"comment":161},20,17,"提出CNN-Transformer-LSTM混合框架用于棉花叶病识别，方法有创新但属实验室验证阶段，产业影响有限。",[163],{"name":155,"url":152},[26,27,28,126,165],"棉花病害识别",[167,168],"棉花叶病 CNN Transformer LSTM","EfficientNet 棉花病害检测","棉花叶病CNNTransformerLSTM-2940","10.53365\u002Fnrfhh.1816",{"doi":170,"openalex_id":172,"authors":173,"venue":155,"cited_by_count":35,"oa_url":9,"card":176,"direction":108,"ingested_from":56},"W7213544712",[174],{"name":175,"orcid":9},"Prajakta Sunil Gupta",{"tldr":177,"method":178,"finding":179,"direction":54,"opportunity":180},"提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。","EfficientNet提取特征，Transformer自注意力与LSTM建模，","混合模型分类性能优于传统CNN，能同时学习局部特征、全局上下文和序列依赖。","可融合IoT环境数据与时间序列，开展病害进展预测和早期预警系统研究。","2026-09-19T23:30:19.558449Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":11,"category":12,"cover_url":9,"hotness":189,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":198,"search_phrases":200,"slug":203,"view_count":35,"doi":204,"paper":205,"created_at":215},2934,"A CNN-Based Approach for Leaf Disease Prediction in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825329","Plants play a crucial role in sustaining life by serving as a primary source of energy and mitigating global warming. However, they are increasingly vulnerable to diseases such as bacterial spot, late blight, and Septoria leaf spot, which significantly impact crop yield and agricultural productivity. Early and accurate detection of these diseases is essential for effective disease management and improved agricultural outcomes. This project aims to develop a deep learning-based approach for detecting plant leaf diseases using Convolutional Neural Networks (CNN). By leveraging benchmark datasets, the proposed CNN model demonstrates superior performance compared to traditional machine learning techniques, achieving an accuracy of 92%, precision of 89%, F1-score of 93%, and recall of 92.47%. The results highlight the effectiveness of CNN in automating disease identification, enabling timely intervention, and promoting sustainable agricultural practices.","植物在维持生命方面发挥着至关重要的作用，既是主要的能量来源，又能缓解全球变暖。然而，植物日益受到细菌性斑点病、晚疫病和壳针孢叶斑病等病害的威胁，严重影响作物产量和农业生产率。早期准确地检测这些病害对于有效防控病害和改善农业成果至关重要。本项目旨在开发一种基于深度学习的方法，利用卷积神经网络（CNN）检测植物叶片病害。通过利用基准数据集，所提出的CNN模型展现出优于传统机器学习技术的性能，达到了92%的准确率、89%的精确率、93%的F1分数和92.47%的召回率。结果表明，CNN在自动化病害识别方面具有显著效果，能够实现及时干预并促进可持续农业实践。","Zenodo (CERN European Organization for Nuclear Research)",25,65,{"impact":72,"substance":17,"depth":19,"authority":72,"freshness":192,"relevant":21,"comment":193},9,"基于CNN的叶片病害识别研究，方法常规、数据集为公开基准，准确率92%属中等水平，对智慧农业植保场景有一定参考价值但缺乏突破性。",[195,196],{"name":188,"url":185},{"name":188,"url":197},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825330",[26,27,28,199],"植物病害识别",[201,202],"CNN 植物叶片病害 识别","卷积神经网络 作物病害 检测","CNN植物叶片病害识别-2934","10.5281\u002Fzenodo.22825329",{"doi":204,"openalex_id":206,"authors":207,"venue":188,"cited_by_count":35,"oa_url":185,"card":210,"direction":108,"ingested_from":56},"W7213587327",[208],{"name":209,"orcid":9},"B.Yashmal Sai, K.Karthik, K.Neeraj, G. Mahabub Subhani",{"tldr":211,"method":212,"finding":213,"direction":54,"opportunity":214},"用CNN对植物叶片病害进行自动识别，在基准数据集上取得92%准确率。","基于卷积神经网络，使用植物叶片病害基准数据集训练与评估。","CNN优于传统机器学习方法，准确率92%、F1值93%，可支持及时干预。","可探索轻量化CNN在田间移动端实时检测，并结合多病害与早期症状识别。","2026-09-19T23:30:11.855376Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":9,"content":9,"source_name":221,"source_url":9,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":226,"tags":228,"search_phrases":231,"slug":234,"view_count":35,"doi":9,"paper":235,"created_at":243},2904,"Decoupled Foundation Models:基于YOLO26m+SAM2+DINOv2的湿度诱导番茄叶坏死实例分割与检测,登MDPI Agriculture 16(18)1997","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1997","本研究针对温室番茄相对湿度过高引发的非生物胁迫(生理性叶坏死,与生物感染症状相似),提出多步AI管道自动化分割与分类坏死叶斑。采集218张RGB图像、3218个标注(棕色坏死斑\u002F黄色坏死斑\u002F无坏死),系统评估6种端到端实例分割管道(YOLO26m检测+SAM2零样本分割+微调DINOv2或EfficientNet-B3分类);微调DINOv2宏F1达0.926,优于EfficientNet-B3、ResNet-50、Swin-Small基线(0.886-0.901);最佳配置mAP@50=0.828,较YOLO26m单模型提升约8%。","MDPI Agriculture","2026-09-17T00:00:00Z",78,{"impact":119,"substance":18,"depth":17,"authority":120,"freshness":192,"relevant":21,"comment":225},"方法组合新颖、数据规模与对比基线扎实，对温室番茄生理性叶坏死自动识别有实用价值，值得进入每日精选。",[227],{"name":221,"url":219},[26,27,229,230,29],"设施农业","番茄",[232,233],"番茄叶坏死 实例分割","农业人工智能 智慧农业 病害识别 设施农业","番茄叶坏死实例分割-2904",{"doi":9,"openalex_id":9,"authors":236,"venue":9,"cited_by_count":35,"oa_url":9,"card":237,"direction":54,"ingested_from":242},[],{"tldr":238,"method":239,"finding":240,"direction":54,"opportunity":241},"用YOLO26m+SAM2+DINOv2多步管道分割并分类高湿诱导的番茄叶坏死斑。","218张RGB图像、3218个标注，评估6种实例分割管道并微调DINOv2分类。","微调DINOv2宏F1达0.926，最佳配置mAP@50=0.828，较单模型提升约8%。","可探索零样本基础模型在多种非生物胁迫症状上的泛化与轻量化温室部署。","agent","2026-09-19T00:06:09.021594Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":9,"content":9,"source_name":249,"source_url":247,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"search_phrases":259,"slug":262,"view_count":35,"doi":263,"paper":264,"created_at":278},2743,"An interpretable deep embedding framework for data-driven discovery of crop families","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102571","An interpretable deep embedding framework for data-driven discovery of crop families。Smart Agricultural Technology","Smart Agricultural Technology","2026-09-14T00:00:00Z",63,{"impact":20,"substance":17,"depth":119,"authority":120,"freshness":20,"relevant":21,"comment":253},"提出可解释深度嵌入框架用于作物科属的数据驱动发现，方法新颖但属学术论文，产业影响有限，时效性较好。",[255],{"name":249,"url":247},[26,27,257,28,258],"种业振兴","作物分类",[260,261],"农业人工智能 作物分类 智慧农业 深度学习","农业人工智能 作物分类","农业人工智能作物分类智慧农业深度学习-2743","10.1016\u002Fj.atech.2026.102571",{"doi":263,"openalex_id":265,"authors":266,"venue":249,"cited_by_count":35,"oa_url":247,"card":9,"direction":9,"ingested_from":56},"W7212949620",[267,270,273,276],{"name":268,"orcid":269},"Ibrahim Nasir Mahmood","https:\u002F\u002Forcid.org\u002F0000-0002-5727-3953",{"name":271,"orcid":272},"Gergely Bencsik","https:\u002F\u002Forcid.org\u002F0000-0002-2381-3480",{"name":274,"orcid":275},"Mustafa Ali Abuzaraida","https:\u002F\u002Forcid.org\u002F0000-0002-9327-8639",{"name":277,"orcid":9},"Zita Szépréti","2026-09-17T23:30:03.574747Z"]