In this paper, an algorithm for classifying five
different varieties of rice, using the color and texture features is
presented. The proposed algorithm consists of several steps:
image acquisition, segmentation, feature extraction, feature
selection, and classification. Sixty color and texture features were
extracted from rice kernels. The Set of features contained
redundant, noisy or even irrelevant information so features were
examined by four different algorithms. Finally twenty-two features
were selected as the superior ones. A back propagation neural
network-based classifier was developed to classify rice varieties.
The overall classification accuracy was achieved as 96.67%.