Image processing based ann with Bayesian regularization learning algorithm for classification of wheat grains

In this paper, an image processing technique (IPT) based artificial neural network (ANN) model using Bayesian regularization (BR) learning algorithm is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the ANN-BR model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then ANN-BR model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The ANN-BR model numerically calculate the outputs with mean absolute error (MAE) of 0.017 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based ANN-BR model can be successfully applied to classification of wheat grains.