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Plant diseases are not just a local concern but pose significant threats to global food security and the sustainability of agriculture. They result in substantial yield losses, compromise crop quality, and impose daunting economic challenges on farmers worldwide. Traditional disease detection methods, reliant on manual visual inspection, are not only labor-intensive but also time-consuming. However, the advent of Deep Learning (DL) technology, offered solutions to these issues. DL algorithms, particularly Convolutional Neural Networks (CNN), have emerged as powerful tools capable of analyzing images of diseased plants, and accurately identifying pathogens or abnormalities. This study represents the potential of DL in agriculture. By evaluating some of the CNN architectures, AlexNet, VGG16, InceptionV3, and MobileNetV2, utilizing a dataset of 38 plant diseases and healthy classes. The results are compelling where AlexNet achieved the best Testing Accuracy (TA) of 94.55%. For, MobileNetV2, InceptionV3, and VGG16 achieving a TA of 92.92%, 90.72%, and 90.23%, respectively.

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Plant Disease Detection Using CNN Architectures: A Comparative Evaluation

  • 会议论文

  • 发表日期：

    2025年

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  • Hanae Al Kaddouri,
  • Abdelmalek El Mehdi,
  • Youssef Douzi,
  • Jalal Blaacha,
  • Hind Messbah,
  • Hajar Hamdaoui,
  • Yassine Zarrouk

摘要

Plant diseases are not just a local concern but pose significant threats to global food security and the sustainability of agriculture. They result in substantial yield losses, compromise crop quality, and impose daunting economic challenges on farmers worldwide. Traditional disease detection methods, reliant on manual visual inspection, are not only labor-intensive but also time-consuming. However, the advent of Deep Learning (DL) technology, offered solutions to these issues. DL algorithms, particularly Convolutional Neural Networks (CNN), have emerged as powerful tools capable of analyzing images of diseased plants, and accurately identifying pathogens or abnormalities. This study represents the potential of DL in agriculture. By evaluating some of the CNN architectures, AlexNet, VGG16, InceptionV3, and MobileNetV2, utilizing a dataset of 38 plant diseases and healthy classes. The results are compelling where AlexNet achieved the best Testing Accuracy (TA) of 94.55%. For, MobileNetV2, InceptionV3, and VGG16 achieving a TA of 92.92%, 90.72%, and 90.23%, respectively.