Deep Learning for Tomato Disease Detection with YOLOv8
Received: 13 February 2024 | Revised: 28 February 2024 | Accepted: 29 February 2024 | Online: 7 March 2024
Corresponding author: Hafedh Mahmoud Zayani
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.
Keywords:
disease detection tomato, deep learning, YOLOv8Downloads
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Copyright (c) 2024 Hafedh Zayani, Ikhlass Ammar, Refka Ghodhbani, Albia Maqbool, Taoufik Saidani, Jihane Ben Slimane, Amani Kachoukh, Marouan Kouki, Mohamed Kallel, Amjad A. Alsuwaylimi, Sami Mohammed Alenezi
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