Research on classification of liquor hops based on convolution neural network

Authors

PAN Bin, School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China; Sichuan Key Laboratory of Artificial Intelligence, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China
HAN Qiang, School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China; Sichuan Key Laboratory of Artificial Intelligence, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China
YAO Ya-chuan, School of Physics and Electrical Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China

Abstract

Objective: This study focuses on realizing the automatic classification of liquor flowers and then improving the real-time and stability of liquor picking. Methods: The machine vision combined with convolutional neural network was used to replace human eyes for liquor picking. Comparing with many image classification methods, the superiority of the improved algorithm was verified. Results: The results showed that the classification accuracy of the model based on the improved Vgg16 convolutional neural network plus transferring-learning method was up to 96.69%. Conclusion: This method can be used in the real-time classification of Baijiu hops stably.

Publication Date

10-28-2021

First Page

30

Last Page

37,88

DOI

10.13652/j.issn.1003-5788.2021.10.006

Recommended Citation

Bin, PAN; Qiang, HAN; and Ya-chuan, YAO (2021) "Research on classification of liquor hops based on convolution neural network," Food and Machinery: Vol. 37: Iss. 10, Article 6.
DOI: 10.13652/j.issn.1003-5788.2021.10.006
Available at: https://www.ifoodmm.cn/journal/vol37/iss10/6

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