An Automated Image Segmentation, Annotation, and Training Framework of Plant Leaves by Joining the SAM and the YOLOv8 Models
Year: 2025
Type: article
Abstract: 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.
Source: Agronomy
Authors: Lumiao Zhao, Kubwimana Olivier, Liping Chen
Institutions: Tarim University, Xinjiang University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4409942489