Cotton Appearance Grade Classification Based on Machine Learning

EasyChair Preprint 1614

Abstract

In recent years, due to the rapid development of Chinese textile industry, the domestic demand for cotton increases sharply. Conversely, the cotton plantation area increasingly dwindled, resulting in the constant rise of cotton imports. China, as a great cotton importer, has classified manually the cotton grades for a long time, which not only results in a consumption of labor and financial resources, but also leads to some mistakes generated by the labor’s subjective evaluation. This paper presents a method for automatic cotton classification for different appearance grades.  Based on a comprehensive comparison, our method performs better in the classification of cotton appearance grades. PCANet feature recognition with basic impurity identification achieves the best performance.

Keyphrases: Cotton Grade, automatic classification, deep learning, machine learning

@booklet{EasyChair:1614,
  author    = {Lv Yan and Gao Ying and Eric Rigall and Qi Lin and Gao Feng and Dong Junyu},
  title     = {Cotton Appearance Grade Classification Based on Machine Learning},
  howpublished = {EasyChair Preprint 1614},
  year      = {EasyChair, 2019}}