Tea category classification via 5-layer customized convolutional neural network
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...
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| Main Authors: | , , |
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| Format: | Book |
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European Alliance for Innovation (EAI),
2021-05-01T00:00:00Z.
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| Summary: | 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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| Item Description: | 10.4108/eai.5-5-2021.169811 2032-9253 |