基于ResNet卷积神经网络的绿茶种类识别模型构建

doi:10.13305/j.cnki.jts.2021.02.008

Abstract

Abstract: Green tea is the tea with the largest variety and output in China. Its appearance is an important basis for its classification. Image classification is one of the core technologies of computer vision, but its application in tea field is almost blank. Tea recognition still relies on the sensory evaluation methods by experts. This study collected 1713 pictures of 8 kinds of green tea (Lishui Xiangcha, Xinyang Maojian, Lu'an Guapian, Taiping Houkui, Anji Baicha, Biluochun, Zhuyeqing and Longjing). Based on the convolutional neural network, we explored the effects of ResNet model depth from the perspectives of model convergence speed, size, efficiency and identification balance. Finally, the ResNet-18 and SGD optimization algorithms were selected and a deep learning model was established to distinguish 8 kinds of green tea. The accuracy reached 90.99%, the recognition time of the single picture was only 0.098 s, and the model size was 43.7 MB. This paper provided the foundation for constructing a tea visual recognition model and applying it to the mobile terminals and provided a new accurate and efficient method for tea recognition.

Key words: convolutional neural network, deep learning, classification of green tea

CLC Number:

S571.1
TP183

ZHANG Yi, ZHAO Zhumeng, WANG Xiaochang, FENG Haiqiang, LIN Jie. Construction of Green Tea Recognition Model Based on ResNet Convolutional Neural Network[J]. Journal of Tea Science, 2021, 41(2): 261-271.

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Construction of Green Tea Recognition Model Based on ResNet Convolutional Neural Network

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