Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques
系统综述目标检测三十年演进，从传统方法到深度学习、Transformer与大模型。
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#计算机视觉
AI-Enabled Smart and Precision Agriculture: Smart Sensors, IoT, Deep Learning, Computer Vision, and Decision Support: A Comprehensive Survey
综述AI赋能精准农业的传感、物联网、深度学习与决策支持，并提出参考架构。
Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee
提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。
Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound
提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。
YOLO-Based Real-Time Object Detection Using Computer Vision for Robotics
提出将轻量YOLO检测器与机器人相机集成的框架，并定义原型评估方案。
A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification
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。
International Journal of Scientific Research in Computer Science Engineering and Information Technology·10月18日 · 周日0 分 论文智慧农业 / 农业物联网阅读 0
Food Recognition and Nutrition Estimator Using Computer Vision and Machine Learning
开发CrunchUP网页应用，拍照识别食物并估算营养，结合个人目标给出膳食建议。
牛肉牛区域特征计算机视觉分析:基于 YOLO 与深度估计的牛体况自动评分
用YOLO检测与深度估计提取牛体区域特征，实现肉牛体况自动评分。
Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time
Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time。Precision Agriculture
A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments
综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。
Traffic surveillance video-based a unified road accident detection, classification, and vehicle tracking framework using an optimized spatio-temporal attention mechanism
提出融合时空注意力Yolov9与DeepSORT的交通事故检测、分类与车辆跟踪框架。
Biology-aware association for identity-consistent leaf tracking in top-down plant phenotyping
提出融合外观与三种生物几何线索的关联模块，实现叶片身份一致跟踪。
Deep learning-based estimation of Kent mango yield by commercial export category under real field conditions in northern Peru
开发深度学习模型在真实田间条件下检测并分类肯特芒果为三个出口等级，实现产量估计。
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。
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。
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。
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。
Phage phenotyping by measuring plaque expansion dynamics
用平板扫描仪连续追踪噬菌体噬菌斑扩展，建立定量表型新方法。
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。
Identifikasi Citra Digital dalam Kesalahan Gerakan Push-Up pada Pemula Menggunakan Metode Pose Estimation
用MediaPipe姿态估计与规则专家系统实时检测初学者俯卧撑动作错误。