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In modern agriculture, the early and accurate identification of plant diseases is essential for enabling rapid responses that safeguard crop quality and yield. Deep learning, particularly Convolutional Neural Networks (CNNs), has demonstrated significant potential in addressing this challenge. In this study, we propose a CNN-based model for identifying and classifying tomato leaf diseases using a publicly available dataset. The performance of the proposed CNN model was evaluated and compared with that of existing pre-trained CNN models—VGG16, VGG19, InceptionV3, and Xception—which have proven effective in image classification tasks. The results show that the proposed model achieves high performance in classifying tomato leaf diseases, reaching an accuracy above 95% on both the training and test datasets.

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Classification of Foliar Diseases in Tomato Crops Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Baltazar López-Velasco,
  • Juan Carlos Olguin-Rojas,
  • Agustín Ruiz-Garcia

摘要

In modern agriculture, the early and accurate identification of plant diseases is essential for enabling rapid responses that safeguard crop quality and yield. Deep learning, particularly Convolutional Neural Networks (CNNs), has demonstrated significant potential in addressing this challenge. In this study, we propose a CNN-based model for identifying and classifying tomato leaf diseases using a publicly available dataset. The performance of the proposed CNN model was evaluated and compared with that of existing pre-trained CNN models—VGG16, VGG19, InceptionV3, and Xception—which have proven effective in image classification tasks. The results show that the proposed model achieves high performance in classifying tomato leaf diseases, reaching an accuracy above 95% on both the training and test datasets.