Deep Learning for Tomato Disease Detection with YOLOv8
Hafedh Mahmoud Zayani, I. Ben Ammar, Refka Ghodhbani, Albia Maqbool, Taoufik Saidani, Jihane Ben Slimane, Amani Kachoukh, Marouan Kouki, Mohamed Kallel, Amjad A. Alsuwaylimi, Sami Mohammed Alenezi
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.