In this paper, an algorithm for identifying five different varieties of rice, using the morphological features is presented. The proposed algorithm consists of several steps: image acquisition, segmentation, feature extraction, feature selection, and classification. Eighteen morphological 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 six 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 98.4%.
