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Research in sustainable agriculture is advancing with AI-driven plant disease diagnosis. Potato crop yield is significantly affected by diseases like early and late blight. This study explores deep learning techniques for automated potato leaf disease classification, employing pre-trained models (InceptionV3-ResNet50, NasNetMobile) and a sequential custom CNN model. By using Artificial Intelligence, we can efficiently detect this kind of disease. It will also help many potato farmers worldwide to detect blight disease automatically without any external help. The custom CNN model outperformed others, achieving a classification accuracy of 97% on the test dataset. The experimental analysis, dataset, pre-processing methods, model architectures, and performance evaluation metrics are thoroughly discussed in this paper.

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Deep Learning-Based Potato Leaf Disease Classification Using a Custom CNN

  • 会议论文

  • 发表日期：

    2025年

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  • Syed Muntasin Fayaz,
  • Md. Abu Raihan,
  • Md. Iftekhar Hossain Tushar

摘要

Research in sustainable agriculture is advancing with AI-driven plant disease diagnosis. Potato crop yield is significantly affected by diseases like early and late blight. This study explores deep learning techniques for automated potato leaf disease classification, employing pre-trained models (InceptionV3-ResNet50, NasNetMobile) and a sequential custom CNN model. By using Artificial Intelligence, we can efficiently detect this kind of disease. It will also help many potato farmers worldwide to detect blight disease automatically without any external help. The custom CNN model outperformed others, achieving a classification accuracy of 97% on the test dataset. The experimental analysis, dataset, pre-processing methods, model architectures, and performance evaluation metrics are thoroughly discussed in this paper.