An Automated Image Segmentation, Annotation, and Training Framework of Plant Leaves by Joining the SAM and the YOLOv8 Models

Recognizing plant leaves in complex agricultural scenes is challenging due to high manual annotation costs and real-time detection demands. Current deep learning methods, such as YOLOv8 and SAM, face trade-offs between annotation efficiency and inference speed. This paper proposes an automated framework integrating SAM for offline semantic segmentation and YOLOv8 for real-time detection. SAM generates pixel-level leaf masks, which are converted to YOLOv8-compatible bounding boxes, eliminating manual labeling. Experiments on three plant species show the framework achieves 87% detection accuracy and 0.03 s per image inference time, reducing annotation labor by 100% compared to traditional methods. The proposed pipeline balances high-quality annotation and lightweight detection, enabling scalable smart agriculture applications.