FM-SAM: individual tree crown delineation and classification based on Segmentation Anything Model (SAM) and YOLOv10 in UAV imagery for forest monitoring

Abstract

Forest monitoring plays a crucial role in the sustainable management and conservation of natural resources, with tree crown delineation being a key task for assessing forest structure and tree health. This study presents a novel framework, FM-SAM, integrating YOLOv10 and the Segment Anything Model (SAM) for precise tree crown segmentation and species identification in UAV imagery. The framework combines YOLOv10's real-time detection capabilities with SAM's strong segmentation, significantly improving segmentation and classification accuracy. SAM is also employed as a semi-supervised branch in FM-SAM to expedite the annotation process, offering a solution that reduces the need for extensive manual labeling. Experimental results on the MixedForestDataset, comprising both coniferous and broadleaf tree species, demonstrate that FM-SAM outperforms traditional deep learning frameworks such as DeepLabv3 and YOLO-based models, achieving high accuracy, precision, and recall values. The proposed method excels in complex forest environments, enhancing the effectiveness of tree crown delineation and classification for large-scale forest monitoring applications.