Deep Learning for Tomato Disease Detection with YOLOv8

Abstract

Tomato production plays a crucial role in Saudi Arabia, with significant yield variations due to factors such as diseases. While automation offers promising solutions, accurate disease detection remains a challenge. This study proposes a deep learning approach based on the YOLOv8 algorithm for automated tomato disease detection. Augmenting an existing Roboflow dataset, the model achieved an overall accuracy of 66.67%. However, class-specific performance varies, highlighting challenges in differentiating certain diseases. Further research is suggested, focusing on data balancing, exploring alternative architectures, and adopting disease-specific metrics. This work lays the foundation for a robust disease detection system to improve crop yields, quality, and sustainable agriculture in Saudi Arabia.