Classification of various rice types and determination of its quality is a major issue in the scientific and commercial fields associated with modern agriculture. In recent years, various image processing techniques are used to identify different types of agricultural products. There are also various color and texture-based features in order to achieve the desired results in this area. In this paper, the problem of rice categorization and quality detection using compressive sensing concepts is considered. This issue includes sparse representation and dictionary learning techniques to achieve over-complete models and represent the structural content of rice variety. Also, dictionaries are learned in such a way to have the least coherence values to each other. The results of the proposed classifier based on the learned models are compared with the results obtained from the neural network and support vector machine classifiers. Simulation results show that the proposed method based on the combinational features is able to identify the type of rice grain and determine its quality with high accuracy rate.
