[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1623":3,"related-1623":99},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":98},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。",null,"Frontiers in Artificial Intelligence","2026-09-03T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,12,7,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","绿豆","深度学习","病害识别",{"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},[45,52,59],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},9,true,[],[30,50],"deep learning",[28,30,31],{"code":53,"score":21,"supported":47,"title":54,"abstract":55,"tags":58},"I1",[],[56,57],"精准农业","precision agriculture",[27],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"A5",[],[],[29],[65,66],"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",3,"10.3389\u002Ffrai.2026.1848787",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":88,"oa_url":89,"card":90,"direction":96,"ingested_from":97},"W7207845216",[73,75,77,80,82,84,86],{"name":74,"orcid":9},"Shail Bala",{"name":76,"orcid":9},"SI Harlapur",{"name":78,"orcid":79},"Aditya Kamalakar Kanade","https:\u002F\u002Forcid.org\u002F0009-0004-8685-3326",{"name":81,"orcid":9},"M. P. Potdar",{"name":83,"orcid":9},"Gurupada Balol",{"name":85,"orcid":9},"VB Kuligod",{"name":87,"orcid":9},"Lingareddy Usha Rani",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1848787\u002Fpdf",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"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","农业人工智能与决策模型","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","openalex","2026-09-04T23:30:11.523875Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,157,223,285,339,404],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":122,"search_phrases":133,"slug":135,"view_count":136,"doi":137,"paper":138,"created_at":156},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":20,"substance":112,"depth":113,"authority":114,"freshness":46,"relevant":22,"comment":115},20,17,14,"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。",[117],{"name":108,"url":105},[27,28,30,31,119],"巴旦木",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":123},{"candidates":124,"agricultural_evidence":47},[125,129],{"code":33,"score":113,"supported":47,"title":126,"abstract":127,"tags":128},[50],[30,50],[28,30,31],{"code":53,"score":22,"supported":14,"title":130,"abstract":131,"tags":132},[],[],[27],[134,65],"巴旦木 病害 深度学习","巴旦木病害深度学习-3256",4,"10.1016\u002Fj.compag.2026.112464",{"doi":137,"openalex_id":139,"authors":140,"venue":108,"cited_by_count":88,"oa_url":105,"card":151,"direction":94,"ingested_from":97},"W7213988471",[141,144,147,149],{"name":142,"orcid":143},"Abebaw Degu Workneh","https:\u002F\u002Forcid.org\u002F0000-0001-7694-1577",{"name":145,"orcid":146},"Badr Elkari","https:\u002F\u002Forcid.org\u002F0000-0002-0893-783X",{"name":148,"orcid":9},"Meryam El Mouhtadi",{"name":150,"orcid":9},"Mohammad Furqan Ali",{"tldr":152,"method":153,"finding":154,"direction":94,"opportunity":155},"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":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":173,"secondary_topics":179,"classification":181,"search_phrases":198,"slug":201,"view_count":202,"doi":203,"paper":204,"created_at":222},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",80,{"impact":19,"substance":18,"depth":19,"authority":114,"freshness":167,"relevant":22,"comment":168},8,"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。",[170],{"name":163,"url":160},[27,28,30,31,172],"苹果病害",{"code":174,"slug":175,"name":176,"path":177,"parent_code":178},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[180],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":182},{"candidates":183,"agricultural_evidence":47},[184,190,194],{"code":174,"score":17,"supported":47,"title":185,"abstract":187,"tags":189},[186],"apple",[188,186],"苹果",[172],{"code":33,"score":46,"supported":47,"title":191,"abstract":192,"tags":193},[],[30,50],[28,30,31],{"code":53,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[27],[199,200],"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",5,"10.1007\u002Fs11119-026-10451-5",{"doi":203,"openalex_id":205,"authors":206,"venue":163,"cited_by_count":88,"oa_url":160,"card":217,"direction":94,"ingested_from":97},"W7213537759",[207,209,212,214],{"name":208,"orcid":9},"Kangrui Han",{"name":210,"orcid":211},"Hao Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9879-2960",{"name":213,"orcid":9},"Kari Peter",{"name":215,"orcid":216},"Long He","https:\u002F\u002Forcid.org\u002F0000-0001-9781-6062",{"tldr":218,"method":219,"finding":220,"direction":94,"opportunity":221},"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",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":9,"source_name":229,"source_url":226,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":232,"sources":235,"tags":237,"primary_topic":240,"secondary_topics":245,"classification":247,"search_phrases":265,"slug":268,"view_count":88,"doi":269,"paper":270,"created_at":284},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":20,"substance":19,"depth":233,"authority":13,"freshness":167,"relevant":22,"comment":234},16,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[236],{"name":229,"url":226},[27,28,30,238,239],"番茄病害","图像识别",{"code":241,"slug":242,"name":243,"path":244,"parent_code":178},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[246],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":248},{"candidates":249,"agricultural_evidence":47},[250,256,260],{"code":241,"score":17,"supported":47,"title":251,"abstract":253,"tags":255},[252],"tomato",[254,252],"番茄",[238],{"code":33,"score":46,"supported":47,"title":257,"abstract":258,"tags":259},[],[30,50],[28,239,30],{"code":53,"score":21,"supported":47,"title":261,"abstract":262,"tags":264},[],[27,263],"smart agriculture",[27],[266,267],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":269,"openalex_id":271,"authors":272,"venue":229,"cited_by_count":88,"oa_url":278,"card":279,"direction":96,"ingested_from":97},"W7222977034",[273,275],{"name":274,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":276,"orcid":277},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":280,"method":281,"finding":282,"direction":94,"opportunity":283},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":291,"source_url":288,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":295,"tags":297,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":324,"slug":327,"view_count":88,"doi":328,"paper":329,"created_at":338},5385,"A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.75s.151","Fruit image analysis has become an essential research area in precision agriculture, enabling automated fruit classification, quality grading, maturity estimation, disease diagnosis, yield prediction, and intelligent harvesting through advances in computer vision and artificial intelligence (AI).Recent progress in imaging technologies, large-scale benchmark datasets, and deep learning has substantially improved the accuracy, robustness, and scalability of automated fruit analysis systems.This survey presents a comprehensive and systematic review of image processing and AI techniques for fruit image analysis.It covers the complete image analysis pipeline, including image acquisition, preprocessing, enhancement, color space transformation, segmentation, feature extraction, and handcrafted texture descriptors.Furthermore, conventional machine learning algorithms are reviewed alongside state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Vision Mamba models, for applications such as fruit recognition, quality assessment, disease detection, ripeness estimation, and object detection.Publicly available benchmark datasets, evaluation metrics, and performance comparison strategies are also summarized to facilitate objective assessment of existing approaches.In addition, the survey discusses key research challenges, including illumination variations, occlusion, complex orchard environments, limited annotated datasets, computational complexity, and domain adaptation, while highlighting emerging research trends such as self-supervised learning, multimodal learning, explainable AI, lightweight deep networks, foundation models, and edge intelligence for real-time deployment.By providing a unified review of methodologies, datasets, evaluation protocols, challenges, and future research directions, this survey offers a comprehensive reference for researchers and practitioners developing intelligent and scalable fruit image analysis systems for precision agriculture.","水果图像分析已成为精准农业中的一个重要研究领域，借助计算机视觉和人工智能（AI）的进步，实现了果实自动分类、品质分级、成熟度估计、病害诊断、产量预测和智能采收。成像技术、大规模基准数据集和深度学习的最新进展显著提升了自动化水果分析系统的准确性、鲁棒性和可扩展性。本综述对用于水果图像分析的图像处理和AI技术进行了全面而系统的回顾。内容涵盖完整的图像分析流程，包括图像采集、预处理、增强、颜色空间变换、分割、特征提取和手工纹理描述符。此外，综述了传统机器学习算法以及最先进的深度学习架构，包括卷积神经网络（CNN）、视觉Transformer（ViT）和视觉Mamba模型，用于果实识别、品质评估、病害检测、成熟度估计和目标检测等应用。同时总结了公开可用的基准数据集、评价指标和性能比较策略，以便对现有方法进行客观评估。此外，本综述讨论了关键研究挑战，包括光照变化、遮挡、复杂果园环境、标注数据集有限、计算复杂度和域适应，并重点介绍了新兴研究趋势，如自监督学习、多模态学习、可解释AI、轻量级深度网络、基础模型和面向实时部署的边缘智能。通过提供方法、数据集、评价协议、挑战和未来研究方向的统一综述，本综述为开发面向精准农业的智能且可扩展的水果图像分析系统的研究人员和实践者提供了全面的参考。","International Journal of Drug Delivery Technology",56,{"impact":167,"substance":19,"depth":233,"authority":100,"freshness":167,"relevant":22,"comment":294},"系统性综述覆盖CNN、ViT、Vision Mamba等前沿方法，信息量足，但期刊权威性一般且偏学术综述，公共价值有限，适合主题聚合而非每日精选头条。",[296],{"name":291,"url":288},[27,28,298,30,299],"计算机视觉","水果品质检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":303},{"candidates":304,"agricultural_evidence":47},[305,315,319],{"code":33,"score":306,"supported":47,"title":307,"abstract":309,"tags":314},28,[50,308],"computer vision",[310,30,311,298,312,50,313,308],"目标检测","机器学习","machine learning","artificial intelligence",[28,30,298],{"code":53,"score":21,"supported":47,"title":316,"abstract":317,"tags":318},[],[56,57],[27],{"code":174,"score":68,"supported":47,"title":320,"abstract":321,"tags":323},[],[322],"orchard",[],[325,326],"水果品质 计算机视觉 深度学习","农业人工智能 水果品质检测 计算机视觉 智慧农业","水果品质计算机视觉深度学习-5385","10.25258\u002Fijddt.16.75s.151",{"doi":328,"openalex_id":330,"authors":331,"venue":291,"cited_by_count":88,"oa_url":288,"card":332,"direction":96,"ingested_from":97},"W7222892831",[],{"tldr":333,"method":334,"finding":335,"direction":336,"opportunity":337},"系统综述深度学习与计算机视觉在水果品质评估与分级中的全流程方法。","综述图像采集到分割、特征提取及CNN、ViT、Vision Mamba等模型与公","深度学习显著提升水果分析精度与鲁棒性，但光照、遮挡和小样本仍是主要瓶颈。","农业遥感与作物表型","可探索自监督与轻量化模型在复杂果园遮挡、小样本场景下的边缘实时部署。","2026-10-20T23:30:10.066042Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":355,"secondary_topics":356,"classification":357,"search_phrases":390,"slug":393,"view_count":22,"doi":394,"paper":395,"created_at":403},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":167,"substance":114,"depth":20,"authority":100,"freshness":46,"relevant":22,"comment":349},"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[351],{"name":345,"url":342},[27,28,353,31,354],"决策支持系统","灌溉管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":358},{"candidates":359,"agricultural_evidence":47},[360,366,373,380,386],{"code":33,"score":361,"supported":47,"title":362,"abstract":364,"tags":365},11,[363],"decision support",[],[28,353,31],{"code":367,"score":21,"supported":47,"title":368,"abstract":369,"tags":372},"H3",[],[370,371],"灌溉","irrigation",[354],{"code":374,"score":100,"supported":47,"title":375,"abstract":376,"tags":379},"H2",[],[377,378],"土壤","soil",[],{"code":381,"score":68,"supported":47,"title":382,"abstract":383,"tags":385},"F3",[],[384],"肥料",[],{"code":53,"score":22,"supported":14,"title":387,"abstract":388,"tags":389},[],[],[27],[391,392],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":394,"openalex_id":396,"authors":397,"venue":345,"cited_by_count":88,"oa_url":342,"card":398,"direction":96,"ingested_from":97},"W7222785888",[],{"tldr":399,"method":400,"finding":401,"direction":94,"opportunity":402},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":9,"source_name":410,"source_url":407,"published_at":411,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":426,"classification":428,"search_phrases":448,"slug":451,"view_count":22,"doi":452,"paper":453,"created_at":486},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":20,"substance":112,"depth":233,"authority":20,"freshness":46,"relevant":22,"comment":414},"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[416],{"name":410,"url":407},[27,28,418,30,419],"边缘计算","玉米病害",{"code":421,"slug":422,"name":423,"path":424,"parent_code":425},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[427],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":429},{"candidates":430,"agricultural_evidence":47},[431,435,439,443],{"code":421,"score":114,"supported":47,"title":432,"abstract":433,"tags":434},[422],[423,422],[],{"code":33,"score":167,"supported":47,"title":436,"abstract":437,"tags":438},[],[30,50],[28,30],{"code":53,"score":21,"supported":47,"title":440,"abstract":441,"tags":442},[],[56,57],[27],{"code":444,"score":22,"supported":14,"title":445,"abstract":446,"tags":447},"I5",[],[],[418],[449,450],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":452,"openalex_id":454,"authors":455,"venue":410,"cited_by_count":88,"oa_url":407,"card":481,"direction":336,"ingested_from":97},"W7222602850",[456,459,462,464,466,469,471,473,476,478],{"name":457,"orcid":458},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":460,"orcid":461},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":463,"orcid":9},"Bhavani K",{"name":465,"orcid":9},"Sailee N. Panchbhai",{"name":467,"orcid":468},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":470,"orcid":9},"Manoj Kumar S B",{"name":472,"orcid":9},"Dharamvir",{"name":474,"orcid":475},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":477,"orcid":9},"Rani Chhagan Dalvi",{"name":479,"orcid":480},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":482,"method":483,"finding":484,"direction":94,"opportunity":485},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","2026-10-17T23:30:28.248539Z"]