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In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images

Author

Listed:
  • Changcai Yang

    (College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Zixuan Teng

    (College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Caixia Dong

    (College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Yaohai Lin

    (College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Riqing Chen

    (College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Jian Wang

    (Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China)

Abstract

A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.

Suggested Citation

  • Changcai Yang & Zixuan Teng & Caixia Dong & Yaohai Lin & Riqing Chen & Jian Wang, 2022. "In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images," Agriculture, MDPI, vol. 12(9), pages 1-11, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1487-:d:916918
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    References listed on IDEAS

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    1. Xiangjin Ran & Linfu Xue & Yanyan Zhang & Zeyu Liu & Xuejia Sang & Jinxin He, 2019. "Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network," Mathematics, MDPI, vol. 7(8), pages 1-16, August.
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