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Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images

Author

Listed:
  • Ganbayar Batchuluun

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Se Hyun Nam

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Chanhum Park

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Kang Ryoung Park

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

Few studies have been conducted on thermal plant images. This is because of the difficulty in extracting and analyzing various color-related patterns and features from the plant image obtained using a thermal camera, which does not provide color information. In addition, the thermal camera is sensitive to the surrounding temperature and humidity. However, the thermal camera enables the extraction of invisible patterns in the plant by providing external and internal heat information. Therefore, this study proposed a novel plant classification method based on both the thermal and visible-light plant images to exploit the strengths of both types of cameras. To the best of our knowledge, this study is the first to perform super-resolution reconstruction using visible-light and thermal plant images. Furthermore, a method to improve the classification performance through generative adversarial network (GAN)-based super-resolution reconstruction was proposed. Through the experiments using a self-collected dataset of thermal and visible-light images, our method shows higher accuracies than the state-of-the-art methods.

Suggested Citation

  • Ganbayar Batchuluun & Se Hyun Nam & Chanhum Park & Kang Ryoung Park, 2022. "Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:76-:d:1014453
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    References listed on IDEAS

    as
    1. Ganbayar Batchuluun & Se Hyun Nam & Kang Ryoung Park, 2022. "Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images," Mathematics, MDPI, vol. 10(21), pages 1-18, November.
    2. Shan-e-Ahmed Raza & Gillian Prince & John P Clarkson & Nasir M Rajpoot, 2015. "Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

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