A machine vision system for classification of wheat and barley grain kernels

This study presents in detail a machine vision system that classifies objects into two classes. The procedure for the classification comprises two stages: a training stage and a testing stage. A feature vector, which is a sorted list of features that maximize the classification power, is computed in the training stage. Object classification was accomplished in the testing stage by means of discriminant analysis (DA) and K-nearest neighbors (K-NN) algorithms. The system was applied to the classification of wheat and barley grain kernels. Results obtained allow the researchers to conclude that in the classification of wheat and grain kernels with the presented system: (i) a high classification accuracy can be obtained; (ii) the employment of morphologic, color, and texture feature types together offers better accuracy than the employment of only one feature type; (iii) the extraction of the maximum radius, the green mean, and the y mean of the gray level co-occurrence matrix (GLCM) for 90° allows the highest classification accuracy; and (iv) the employment of more than three features increases the computational cost and may also reduce the classification accuracy.