Identification of milled rice varieties using machine vision

Abstract

Machine vision detection has the advantages of non-contact, non-destructive, and is suitable for large-scale and high-speed detection. However, existing studies only adopted one or one type image feature in the varieties identification of milled rice, and contained limited explanation of the influence of the image features on the identification accuracy. Thus, this paper developed an identification model for milled rice varieties based on multiple image features and explored the contributions of each image feature on the identification accuracy. The extracted image features were thirteen morphology features, eighteen color features, and four texture features. The training and testing data sets were one hundred and forty milled rice samples. The partial least squares algorithm was used to identify milled rice varieties. The multivariate data analysis was used to get the influence of the image features on the identification accuracy. Experiment results showed that, by selecting twenty features from the original thirty-five features, the identification accuracies were 100%, 70%, 100%, and 100% for milled rice varieties YG, WC, XS, and JN.