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DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

Author

Listed:
  • Yang Chen

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Xiaoyulong Chen

    (College of Tobacco Science, Guizhou University, Guiyang 550025, China)

  • Jianwu Lin

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Renyong Pan

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Tengbao Cao

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Jitong Cai

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Dianzhi Yu

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Tomislav Cernava

    (Institute of Environmental Biotechnology, Graz University of Technology, 8010 Graz, Austria)

  • Xin Zhang

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

Abstract

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

Suggested Citation

  • Yang Chen & Xiaoyulong Chen & Jianwu Lin & Renyong Pan & Tengbao Cao & Jitong Cai & Dianzhi Yu & Tomislav Cernava & Xin Zhang, 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agriculture, MDPI, vol. 12(12), pages 1-22, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2047-:d:987919
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    References listed on IDEAS

    as
    1. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    2. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    3. Jianwu Lin & Xiaoyulong Chen & Renyong Pan & Tengbao Cao & Jitong Cai & Yang Chen & Xishun Peng & Tomislav Cernava & Xin Zhang, 2022. "GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
    4. Shengyi Zhao & Yun Peng & Jizhan Liu & Shuo Wu, 2021. "Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.

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