Tea category classification via 5-layer customized convolutional neural network
DOI:
https://doi.org/10.4108/eai.5-5-2021.169811Keywords:
convolutional neural network, customized convolution neural network, deep learning, tea category classificationAbstract
INTRODUCTION: Green tea, oolong, and black tea are the three most popular teas in the world. If classified tea by manual, it will not only take a lot of time, but also be affected by other factors, such as smell, vision, emotion, etc.
OBJECTIVES: Other methods of tea category classification have the shortcomings of low classification accuracy, weak robustness. To solve the above problems, we proposed a method of deep learning.
METHODS: This paper proposed a 5-layer customized convolutional neural network for 3 tea categories classification.
RESULTS: The experimental results show that the method has fast speed and high accuracy of tea classification, which is 97.96%.
CONCLUSION: Compared with state-of-the-art methods, our method has better performance than six state-of-the-art methods.
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This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
