YOLO-Based Leaf Disease Detection in Smart Agriculture: Evolution, Task Adaptation, and Deployment Challenges
综述了基于YOLO的叶片病害检测的演进、任务适配与部署挑战。
阅读 0
An empirical performance comparison of deep learning models for apple leaf disease recognition
用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。
阅读 0
Deep learning–based computer vision for rapid and accurate quantitative field assessment of powdery mildew severity: model comparison, validation, and agreement with human raters
开发并验证基于深度学习的计算机视觉流程，用手机图像快速定量评估豌豆白粉病严重度。
阅读 3
多尺度异构特征协同的棉花叶片病害检测模型(Multi-Scale Heterogeneous Synergistic Model for Cotton Leaf Disease Detection)
提出MHSF-DETR棉花叶片病害检测模型，提升精度并降低参数量与计算量。
阅读 2
SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION
集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。
阅读 4
Knowledge Distillation for Hybrid CNN–Vision Transformer Architectures: A Systematic Review
系统综述2015-2026年混合CNN-ViT架构的知识蒸馏方法，按迁移方向、知识类型和应用领域分类
阅读 0
Animal welfare in the world of digitalization: Computer Vision in the context of foundational structures
论文论证计算机视觉用于动物福利时，伦理问题须置于社会基础结构中理解。
阅读 1
Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production
综述深度学习与计算机视觉在芒果叶片病害早期识别中的应用，梳理三代技术并展望可持续种植。
2 个信源阅读 2
Automated dairy cattle lameness assessment using a 38-keypoint whole-body YOLOv8 pose framework
构建38关键点全身YOLOv8姿态框架，实现奶牛跛行自动评估并与Sprecher评分比对。
阅读 2
A Comprehensive Review Of Image Processing Techniques: Classical Methods, Deep Learning, Applications, And Recent Advances
综述图像处理从经典方法到深度学习的技术演进、应用与挑战。
2 个信源阅读 4
WeedNet: a foundation model-based global-to-local AI approach for weed species identification and classification
提出WeedNet基础模型，实现全球杂草物种识别与区域微调。
阅读 5
Addressing scale Variation and texture confusion in strawberry phenology detection via a lightweight synergistic network
提出轻量协同网络，解决草莓物候检测中的尺度变化与纹理混淆问题。
阅读 4
Geometric representation-based facial keypoint matching for sheep identification across growth stages
提出基于面部关键点几何表示的绵羊识别方法，可跨生长阶段匹配。
阅读 1
An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction
融合计算机视觉与机器学习，实现高粱病害严重度评估与产量预测。
4 个信源阅读 3
Designing a framework for Sustainable Smart Agriculture with Mobile Robots and CNN
提出移动机器人结合CNN识别花卉的智能园艺框架，测试准确率达95%。
阅读 2
Accelerated development and deployment of computer vision models for invasive aquatic pests
提出一套开发与部署水生入侵物种计算机视觉模型的实用框架，并在新西兰三种入侵物种上验证。
阅读 4
RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery
用冻结DINOv3 ViT微调回归，从微根管图像直接预测根系长度和面积。
2 个信源阅读 5
Brazilian Insect Survey: A Platform for Pest Management
巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。
阅读 3
Computer vision and artificial intelligence system for real-time detection of the use of personal protective equipment in industrial environments
提出基于YOLOv8和树莓派的实时个人防护装备检测系统，准确率96.5%。
阅读 1
AshGdNutDefAM: Machine-learning-based multi-feature framework for nutrient deficiency detection and classification in Ash gourd Plant
提出AshGdNutDefAM多特征机器学习框架，自动检测和分类冬瓜缺镁缺铁症状。
阅读 3