Deep Learning for Tomato Disease Detection with YOLOv8

Authors

  • Hafedh Mahmoud Zayani Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia
  • Ikhlass Ammar Computer Science Department, Faculty of Sciences of Tunis (FST), University of Tunis El Manar, Tunisia | OASIS Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunisia
  • Refka Ghodhbani Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Albia Maqbool Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Taoufik Saidani Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Jihane Ben Slimane Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Amani Kachoukh Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Marouan Kouki Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Mohamed Kallel Department of Physics, Faculty of Sciences and Arts, Northern Border University, Saudi Arabia
  • Amjad A. Alsuwaylimi Department of Information Technology, College of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Sami Mohammed Alenezi Department of Information Technology, College of Computing and Information Technology, Northern Border University, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13584-13591 | April 2024 | https://doi.org/10.48084/etasr.7064

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, YOLOv8

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How to Cite

[1]
H. M. Zayani, “Deep Learning for Tomato Disease Detection with YOLOv8”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13584–13591, Apr. 2024.

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