Wheat Varieties Identification Research Based on Sparse Representation Method of Dictionary Learning

With the rapid development of computer vision technology, the use of machine vision to replace artificial is widely used in product detection and classification. The conventional sparse representation methods need a large number of training samples to improve the ability of sparse representation of a dictionary. This results in a large dictionary size and an immense memory requirement, which often leads to low efficiency in actual applications. In this paper, a novel method of identification and classification of the wheat varieties is given based on the sparse representation method with the dictionary learning technique. In the given method, the K-SVD algorithm is utilized to train the feature dictionary, the number of the atoms in which is effectively reduced, compared with the method of identification and classification of the wheat varieties based on the conventional sparse representation method. The final test simulation verifies the effectiveness and feasibility of the new identification and classification method of wheat varieties and compares it with the conventional identification and classification method of wheat varieties.