Engineering, Technology & Applied Science Research (Apr 2024)

Deep Learning for Tomato Disease Detection with YOLOv8

  • Hafedh Mahmoud Zayani,
  • Ikhlass Ammar,
  • Refka Ghodhbani,
  • Albia Maqbool,
  • Taoufik Saidani,
  • Jihane Ben Slimane,
  • Amani Kachoukh,
  • Marouan Kouki,
  • Mohamed Kallel,
  • Amjad A. Alsuwaylimi,
  • Sami Mohammed Alenezi

DOI
https://doi.org/10.48084/etasr.7064
Journal volume & issue
Vol. 14, no. 2

Abstract

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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.

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