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Early diagnosis of plant diseases boosts agricultural yields. Deep learning in image processing enables this. This study uses MobileNet, an optimized model for resource-limited settings, to automate tomato disease detection. The approach analyzes a dataset of 12,729 images across ten classes, reducing human effort with efficient algorithms. The proposed model achieved 94% accuracy, providing a fast and effective solution for phytopathological diagnosis.

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Tomato Disease Prediction Using Machine Learning with MobileNet

  • 会议论文

  • 发表日期：

    2026年

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  • Bouyaakoubi Fadwa,
  • Aaroud Abdessadek

摘要

Early diagnosis of plant diseases boosts agricultural yields. Deep learning in image processing enables this. This study uses MobileNet, an optimized model for resource-limited settings, to automate tomato disease detection. The approach analyzes a dataset of 12,729 images across ten classes, reducing human effort with efficient algorithms. The proposed model achieved 94% accuracy, providing a fast and effective solution for phytopathological diagnosis.