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Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears

Author

Listed:
  • Yeong Hyeon Gu

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
    These authors have contributed equally to this work and share first authorship.)

  • Helin Yin

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
    These authors have contributed equally to this work and share first authorship.)

  • Dong Jin

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea)

  • Ri Zheng

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea)

  • Seong Joon Yoo

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

Abstract

Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep features extracted by deep convolutional neural networks and k -nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method.

Suggested Citation

  • Yeong Hyeon Gu & Helin Yin & Dong Jin & Ri Zheng & Seong Joon Yoo, 2022. "Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:300-:d:754144
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    References listed on IDEAS

    as
    1. Helin Yin & Yeong Hyeon Gu & Chang-Jin Park & Jong-Han Park & Seong Joon Yoo, 2020. "Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests," Agriculture, MDPI, vol. 10(10), pages 1-16, September.
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    Cited by:

    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. Zeqing Yang & Zhimeng Li & Ning Hu & Mingxuan Zhang & Wenbo Zhang & Lingxiao Gao & Xiangyan Ding & Zhengpan Qi & Shuyong Duan, 2023. "Multi-Index Grading Method for Pear Appearance Quality Based on Machine Vision," Agriculture, MDPI, vol. 13(2), pages 1-21, January.

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