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Journal of Tea Science ›› 2022, Vol. 42 ›› Issue (3): 387-396.doi: 10.13305/j.cnki.jts.2022.03.002

• Research Paper • Previous Articles     Next Articles

Research on the Classification Method of Tea Buds Combining Improved Capsule Network and Knowledge Distillation

CHEN Xingran1, HUANG Haisong1,*, HAN Zhenggong1, FAN Qingsong1, ZHU Yunwei1, HU Pengfei2,3   

  1. 1. Key laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China;
    2. Guizhou Vocational College of Equipment Manufacturing, Guiyang 551400, China;
    3. Qingzhen Hongfeng Mountain Yun Tea Factory Co., Ltd, Guiyang 551400, China
  • Received:2021-11-30 Revised:2022-02-16 Online:2022-06-15 Published:2022-06-17

Abstract: The accurate classification of different grades of tea buds is very important for the development of the famous tea industry. The use of traditional sensory evaluation methods for sorting makes the results subjective. In this research, a data set was established after tea leaf images were collected, and a new network model, GA-CapsNet, was proposed by combining the ghost attention bottleneck and capsule network. The model was trained by the method of growing knowledge distillation based on the linear decay scaling coefficient, while migrating the parameter matrix of teacher model, the student model was adaptively reduced with iteration. The experimental result shows that, compared with other similar algorithms, the proposed method had excellent classification performance on small-scale data sets. The accuracy, recall and F1-score were 94.97%, 95.51% and 95.24%, respectively. Here, a GA-CapsNet model based on machine vision and deep learning technology was established, which provided a new idea for solving the tea leaf classification problem.

Key words: capsule network, knowledge distillation, attention module, tea bud grading

CLC Number: