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Plant diseases pose a significant threat to global food security, demanding faster and more accessible methods for identification. While deep learning has shown promise in automating plant disease diagnosis from digital images, large model sizes remain a hurdle. This paper proposes a novel lightweight deep learning framework based on CovXNet, a convolutional neural network architecture utilizing efficient depthwise convolutions, combined with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) attention mechanisms for enhanced performance. Our proposed models have been evaluated on the publicly available Plant Village dataset, achieving state-of-the-art performance with 99.37% test accuracy using CovXNet with SE and 99.30% using CovXNet with CBAM across 38 classes. These results demonstrate the effectiveness of our approach in facilitating accurate and efficient plant disease diagnosis, particularly for deployment on resource-constrained devices.

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Attention-Enhanced Multi-dilation CNN for Plant Disease Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Disha Chowdhury,
  • Nomaiya Bashree,
  • Tareque Bashar Ovi,
  • Hussain Nyeem,
  • Md Abdul Wahed,
  • Rawnak Tanzim

摘要

Plant diseases pose a significant threat to global food security, demanding faster and more accessible methods for identification. While deep learning has shown promise in automating plant disease diagnosis from digital images, large model sizes remain a hurdle. This paper proposes a novel lightweight deep learning framework based on CovXNet, a convolutional neural network architecture utilizing efficient depthwise convolutions, combined with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) attention mechanisms for enhanced performance. Our proposed models have been evaluated on the publicly available Plant Village dataset, achieving state-of-the-art performance with 99.37% test accuracy using CovXNet with SE and 99.30% using CovXNet with CBAM across 38 classes. These results demonstrate the effectiveness of our approach in facilitating accurate and efficient plant disease diagnosis, particularly for deployment on resource-constrained devices.