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An Improved EfficientNet for Rice Germ Integrity Classification and Recognition

Author

Listed:
  • Bing Li

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

  • Bin Liu

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

  • Shuofeng Li

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

  • Haiming Liu

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

Rice is one of the important staple foods for human beings. Germ integrity is an important indicator of rice processing accuracy. Traditional detection methods are time-consuming and highly subjective. In this paper, an EfficientNet–B3–DAN model is proposed to identify the germ integrity. Firstly, ten types of rice with different germ integrity are collected as the training set. Secondly, based on EfficientNet–B3, a dual attention network (DAN) is introduced to sum the outputs of two channels to change the representation of features and further focus on the extraction of features. Finally, the network is trained using transfer learning and tested on a test set. Comparing with AlexNet, VGG16, GoogleNet, ResNet50, MobileNet, and EfficientNet–B3, the experimental illustrate that the detection overall accuracy of EfficientNet–B3–DAN is 94.17%. It is higher than other models. This study can be used for the classification of rice germ integrity to provide guidance for rice and grain processing industries.

Suggested Citation

  • Bing Li & Bin Liu & Shuofeng Li & Haiming Liu, 2022. "An Improved EfficientNet for Rice Germ Integrity Classification and Recognition," Agriculture, MDPI, vol. 12(6), pages 1-16, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:863-:d:839406
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    Cited by:

    1. Jinbo Zhou & Shan Zeng & Yulong Chen & Zhen Kang & Hao Li & Zhongyin Sheng, 2023. "A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection," Agriculture, MDPI, vol. 13(1), pages 1-16, January.

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