CDA-YOLOv8: A Model for Instance Segmentation of Grapevine Key Structures in Complex Environments

This study addresses the challenge of instance segmentation for key grapevine structures (Trunk, Branch, and Bud) in complex natural environments, focusing on issues such as varying light conditions, weather, and significant scale variations. We propose an enhanced instance segmentation model named CDA-YOLOv8. Trained on a self-built dataset of 2160 images covering grapevine scenes under diverse lighting conditions, this model integrates three key components: the ACmix module for enhanced global feature modeling, the C2f-DWR module for optimized multi-scale feature extraction, and the CSPPC module for achieving model lightweighting. We evaluate performance using precision/recall and mAP@50, together with the stricter mAP@[50:95], for segmentation quality, and parameters/model size/FPS for deployment efficiency. Experimental results demonstrate that CDA-YOLOv8 achieves 70.1% precision, 74.4% recall, 76.3% mAP@50, and 36.8% mAP@[50:95], with only 3.19 million parameters and a compact model size of 6.49 MB. Compared with the original YOLOv8-seg, CDA-YOLOv8 improves segmentation accuracy while maintaining high efficiency (6.87 FPS). It also delivers better mask quality under stricter overlap criteria, providing quantitative evidence for real-time perception in automated grapevine pruning systems.