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VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition

Author

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  • Xiangpeng Fan

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China)

  • Zhibin Guan

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China)

Abstract

The automatic recognition of crop diseases based on visual perception algorithms is one of the important research directions in the current prevention and control of crop diseases. However, there are two issues to be addressed in corn disease identification: (1) A lack of multicategory corn disease image datasets that can be used for disease recognition model training. (2) The existing methods for identifying corn diseases have difficulty satisfying the dual requirements of disease recognition speed and accuracy in actual corn planting scenarios. Therefore, a corn diseases recognition system based on pretrained VGG16 is investigated and devised, termed as VGNet, which consists of batch normalization (BN), global average pooling (GAP) and L2 normalization. The performance of the proposed method is improved by using transfer learning for the task of corn disease classification. Experiment results show that the Adam optimizer is more suitable for crop disease recognition than the stochastic gradient descent (SGD) algorithm. When the learning rate is 0.001, the model performance reaches a highest accuracy of 98.3% and a lowest loss of 0.035. After data augmentation, the precision of nine corn diseases is between 98.1% and 100%, and the recall value ranges from 98.6% to 100%. What is more, the designed lightweight VGNet only occupies 79.5 MB of space, and the testing time for 230 images is 75.21 s, which demonstrates better transferability and accuracy in crop disease image recognition.

Suggested Citation

  • Xiangpeng Fan & Zhibin Guan, 2023. "VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1606-:d:1217016
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    References listed on IDEAS

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    1. Jingyao Zhang & Yuan Rao & Chao Man & Zhaohui Jiang & Shaowen Li, 2021. "Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477211, April.
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    Cited by:

    1. Zhuoshi Li & Shizheng Qu & Yinghang Xu & Xinwei Hao & Nan Lin, 2024. "Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition," Mathematics, MDPI, vol. 12(3), pages 1-22, January.

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