Tree Detection

Tree detection research focuses on automatically identifying and quantifying trees in various environments using aerial and ground-based imagery, primarily to improve efficiency and safety in forestry and agriculture. Current methods heavily leverage deep learning architectures like YOLO, Faster R-CNN, and RetinaNet, often combined with stereo vision or LiDAR data for accurate localization and measurement of tree characteristics such as size and species. These advancements are significantly impacting fields like precision agriculture, forest fire risk assessment, and urban forestry management by enabling large-scale data collection and analysis previously impossible with manual methods.