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Deep Learning-Based Plant Classification Using Nonaligned 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, Korea)

  • Se Hyun Nam

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

  • Kang Ryoung Park

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

Abstract

There have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are sensitive to light, and cannot be used in environments with low illumination. Although thermal cameras are not susceptible to these drawbacks, they are sensitive to atmospheric temperature and humidity. Moreover, in previous thermal camera-based studies, time-consuming manual analyses were performed. Therefore, in this study, we conducted a novel study by simultaneously using thermal images and corresponding visible light images of plants to solve these problems. The proposed network extracted features from each thermal image and corresponding visible light image of plants through residual block-based branch networks, and combined the features to increase the accuracy of the multiclass classification. Additionally, a new database was built in this study by acquiring thermal images and corresponding visible light images of various plants.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4053-:d:959636
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    References listed on IDEAS

    as
    1. Tej Bahadur Shahi & Chiranjibi Sitaula & Arjun Neupane & William Guo, 2022. "Fruit classification using attention-based MobileNetV2 for industrial applications," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-21, February.
    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.
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    Cited by:

    1. 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.

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