el 21(22): e1

Research Article

Tea category classification via 5-layer customized convolutional neural network

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  • @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},
        volume={7},
        number={22},
        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
Xiang Li1, Mengyao Zhai2, Junding Sun1,*
  • 1: College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, P R China
  • 2: College of Humanities and Education, Hebi Polytechnic, Hebi, Henan, 458030, P R China
*Contact email: sunjd@hpu.edu.cn

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.