A Lightweight System for Strawberry Disease Detection and Segment Based on YOLOv8

Strawberries are highly susceptible to diseases, which significantly affect crop yield and quality. Accurate and early detection of these diseases is vital for mitigating their adverse impacts. This study presents a lightweight strawberry disease detection and segmentation open-sourced system based on the YOLOv8n model. The system can identify and segment seven common strawberry diseases, classifies the severity. Unlike existing models that demand significant computational resources and technical expertise, our solution is designed to be efficient, requiring fewer computational resources while maintaining high accuracy. The open-sourced system is accessible via a user-friendly graphical interface, rather than requiring command-line operations. With an [email protected] score of 0.935 for object detection and 0.930 for object segmentation, the proposed system offers a practical tool for strawberry farmers, reducing labor costs and enhancing disease management efficiency.