ANN-BASED CLASSIFIER TRAINED BY BAYESIAN REGULARIZATION FOR WHEAT GRAINS THROUGH COLOUR FEATURE

Öz

In this paper, a color feature-based classification of the wheat grains into bread and durum using artificial neural network (ANN) model with bayesian regularization (BR) learning algorithm is presented. 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. Data of 3 main colour features (R, G and B) for 200 wheat grains (100 for durum and 100 for bread) are acquired for each grain using image processing techniques (IPTs). Features of R, G and B are separately determined by taking arithmetic average of the pixels within each grain. Several colour features of R/TRGB, G/TRGB, B/TRGB, R-G, G-B and R-B where TRGB is the total of R+G+B are reproduced. Then ANN-BR model input with the 9 colour 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 0.0060 and classify the grains with accuracy of 100% for the testing process. These results show that the ANN-BR model can be successfully applied to classification of wheat grains.

Anahtar Kelimeler

• Classification,wheat grains,image processing technique,artificial neural network,bayesian regularization learning algorithm

Kaynakça

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