Research Article
Tea category classification via 5-layer customized convolutional neural network
@ARTICLE{10.4108/eai.5-5-2021.169811, author={Xiang Li and Mengyao Zhai and Junding Sun}, title={Tea category classification via 5-layer customized convolutional neural network}, journal={EAI Endorsed Transactions on e-Learning: Online First}, volume={}, number={}, publisher={EAI}, journal_a={EL}, year={2021}, month={5}, keywords={convolutional neural network, customized convolution neural network, deep learning, tea category classification}, doi={10.4108/eai.5-5-2021.169811} }- Xiang Li
Mengyao Zhai
Junding Sun
Year: 2021
Tea category classification via 5-layer customized convolutional neural network
EL
EAI
DOI: 10.4108/eai.5-5-2021.169811
Abstract
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.
Copyright © 2021 Xiang Li et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.