LGVM-YOLOv8n: A Lightweight Apple Instance Segmentation Model for Standard Orchard Environments

Accurate fruit target identification is crucial for autonomous harvesting robots in complex orchards, where image segmentation using deep learning networks plays a key role. To address the trade-off between segmentation accuracy and inference efficiency, this study proposes LGVM-YOLOv8n, a lightweight instance segmentation model based on YOLOv8n-seg. LGVM is an acronym for lightweight, GSConv, VoVGSCSP, and M P D I o U , highlighting the key improvements incorporated into the model. The proposed model integrates three key improvements: (1) the GSConv module, which enhances feature interaction and reduces computational cost; (2) the VoVGSCSP module, which optimizes multi-scale feature representation for small objects; and (3) the M P D I o U loss function, which improves target localization accuracy, particularly for occluded fruits. Experimental results show that LGVM-YOLOv8n reduces computational cost by 9.17%, decreases model weight by 7.89%, and improves inference speed by 16.9% compared to the original YOLOv8n-seg. Additionally, segmentation accuracy under challenging conditions (front-light, back-light, and occlusion) improves by 3.28% to 4.31%. Deployment tests on an edge computing platform demonstrate real-time performance, with inference speed accelerated to 0.084 s per image and frame rate increased to 28.73 FPS. These results validated the model’s robustness and adaptability, providing a practical solution for apple-picking robots in complex orchard environments.