A Method for Plant Disease Enhance Detection Based on Improved YOLOv8

This paper presents a method for real-time and accurate identification of plant diseases using the state-of-the-art YOLOv8 (You Only Look Once Version 8) object detection model. Addressing issues of inaccurate detection and unstable accuracy rates encountered in traditional disease diagnosis processes, particularly concerning small-sized disease targets, we introduce an approach that incorporates an attention mechanism for detecting small disease lesions. The experimental dataset comprises annotated image samples of various common plant diseases affecting a range of crops including legumes, strawberries, and tomatoes. After preprocessing and transforming the dataset into a format suitable for input into the YOLOv8 model, and conducting targeted optimization training on the model, we compared its accuracy with commonly used disease detection models. Our identification model demonstrated relatively high efficiency in recognizing diseases in these three types of crops, thus carrying significant research implications for intelligent agricultural monitoring.