Tea category classification via 5-layer customized convolutional neural network - Xiang Li; Mengyao Zhai; Junding Sun (European Alliance for Innovation (EAI), 2021)
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.