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

Year 2017, Volume: 1 Issue: 1, 46 - 53, 27.12.2017

Berat Yıldız , Abdurrahim Toktaş , Enes Yiğit , Ahmet Kayabaşı , Kadir Sabancı , Mustafa Tekbaş

Abstract

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.

Keywords

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

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