Cotton Color Grading with a Neural Network

Abstract

It is well known that disagreements about cotton color grades between high volume instruments and classers are substantial, and these machine-classer disagreements deter full acceptance of machine grading of cotton color. This paper provides first a quantitative analysis of the distributions of these disagreements across all the color grades, both major and subcolor categories. The study proves that the disagreements can be both systematic and random, and further analyzes the possible sources for them. Second, the paper introduces a novel design of a neural network classifier for cotton color classification. This classifier consists of multiple networks performing a two-step classification that identifies major and subcolor categories separately. The classifier can be trained by any desirable data. In this research, it is trained using a set of classers' grades, and it exhibits good generalization for the new testing data. The classifier seems to reduce machine-classer disagreements to a minimal level, which is limited by the classer's reproducibility.