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Deep Learning-Based Plant-Image Classification Using a Small Training Dataset

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

Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers, trees, etc., and their leaves). However, existing plant-based image datasets are generally small. Furthermore, there are limitations in the construction of large-scale datasets. Consequently, previous research on plant classification using small training datasets encountered difficulties in achieving high accuracy. However, research on plant image classification based on small training datasets is insufficient. Accordingly, this study performed classification by reducing the number of training images of plant-image datasets by 70%, 50%, 30%, and 10%, respectively. Then, the number of images was increased back through augmentation methods for training. This ultimately improved the plant-image classification performance. Based on the respective preliminary experimental results, this study proposed a plant-image classification convolutional neural network (PI-CNN) based on plant image augmentation using a plant-image generative adversarial network (PI-GAN). Our proposed method showed the higher classification accuracies compared to the state-of-the-art methods when the experiments were conducted using four open datasets of PlantVillage, PlantDoc, Fruits-360, and Plants.

Suggested Citation

  • Ganbayar Batchuluun & Se Hyun Nam & Kang Ryoung Park, 2022. "Deep Learning-Based Plant-Image Classification Using a Small Training Dataset," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3091-:d:899750
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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.
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