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MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Author

Listed:
  • Dasen Li

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Zhendong Yin

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Yanlong Zhao

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Wudi Zhao

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Jiqing Li

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Tomato disease classification based on images of leaves has received wide attention recently. As one of the best tomato disease classification methods, the convolutional neural network (CNN) has an immense impact due to its impressive performance. However, better performance is verified by independent identical distribution (IID) samples of tomato disease, which breaks down dramatically on out-of-distribution (OOD) classification tasks. In this paper, we investigated the corruption shifts, which was a vital component of OOD, and proposed a tomato disease classification method to improve the performance of corruption shift generalization. We first adopted discrete cosine transform (DCT) to obtain the low-frequency components. Then, the weight of the feature map was calculated by multiple low-frequency components, in order to reduce the influence of high-frequency variation caused by corrupted perturbation. The proposed method, termed as a multiple low-frequency attention network (MLFAnet), was verified by the benchmarking of ImageNet-C . The accuracy result and generalization performance confirmed the effectiveness of MLFAnet. The satisfactory generalization performance of our proposed classification method provides a reliable tool for the diagnosis of tomato disease.

Suggested Citation

  • Dasen Li & Zhendong Yin & Yanlong Zhao & Wudi Zhao & Jiqing Li, 2023. "MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization," Agriculture, MDPI, vol. 13(6), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1140-:d:1158375
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    References listed on IDEAS

    as
    1. Anil Bhujel & Na-Eun Kim & Elanchezhian Arulmozhi & Jayanta Kumar Basak & Hyeon-Tae Kim, 2022. "A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    2. Ewa Ropelewska & Vanya Slavova & Kadir Sabanci & Muhammet Fatih Aslan & Veselina Masheva & Mariana Petkova, 2022. "Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-12, November.
    3. Haotian You & Yufang Lu & Haihua Tang, 2023. "Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    4. Kamal KC & Zhendong Yin & Dasen Li & Zhilu Wu, 2021. "Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ," Agriculture, MDPI, vol. 11(9), pages 1-16, August.
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