Image Cluster Features Shape and Texture Determinants of Rice Quality Using the K Means Algorithm

Abstract

There is a lot of fraud case in the forgery of ricequality by mixing good quality rice with low 
quality rice for increasing price. To protect the community from counterfeiting, we conduct 
research to detect the quality of rice which can later help the community to be able to 
distinguish good and bad quality. This paper presents a low-costimage processing system 
for assessing the quality of rice. Many factors affect the quality of rice such as grain 
fragments, non-uniform color, odor and other factors. This study uses procentage of broken 
rice grains and color uniformity to determine the quality of rice. We propose texture feature 
with Otsu segementation for determining the number of broken grains and color distribution 
for specifying the color uniform. The classification results usingK Fold validation on the 
original data show the results of K-Nearest Neighbor have 99.70% accuracy.