Tea category classification via 5-layer customized convolutional neural network

Li, Xiang and Zhai, Mengyao and Sun, Junding (2021) Tea category classification via 5-layer customized convolutional neural network. EAI Endorsed Transactions on e-Learning. e1. ISSN 2032-9253

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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.

Item Type: Article
Uncontrolled Keywords: convolutional neural network, customized convolution neural network, deep learning, tea category classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:49
Last Modified: 20 Jul 2021 09:49
URI: https://eprints.eudl.eu/id/eprint/4877

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