Development of an automatic system in order to classify the type of rice grains is an interesting research area in the scientific fields associated with the modern agriculture. In the recent years, different techniques have been employed to identify the various types of agricultural products. Also different color-based and texture-based features have been used to yield the desired results in the classification procedure. In this paper, we propose a classification algorithm in order to detect the different rice types by extracting features from the bulk samples. The feature space in this algorithm includes the fractal-based features of the extracted coefficients from the wavelet packet transform analysis. This feature vector is combined with the other texture-based features used to learn a model related to each rice type using the Gaussian mixture model classifier. Also a sparse structured principal component analysis algorithm is applied to reduce the dimension of the feature vector and lead to the precise classification rate with a less computational time. The results of the proposed classifier are compared with those obtained from the other presented classification procedures in this context. The simulation results along with a meaningful statistical test, show that the proposed algorithm based on the combinational features is able to detect precisely the type of rice grains with a more than 99% accuracy. Also the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with a 99.75% average accuracy.
