Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features

Abstract

Wheat is the main ingredient of most common food products in our daily lives and obtaining good quality wheat kernels is an important matter for the production of food supplies. In this study, type-1252 durum wheat kernels which have vast harvest areas in Turkey and is the principal ingredient of pasta and semolina products were examined and classified to obtain top quality wheat kernels based on their vitreousness. Also, top quality provision of food supplies means that the products must be refined from all foreign materials so a classification process has been applied to extract foreign materials from wheat kernels. In this study, we have used a total of 236 morphological, colour, wavelet and gaborlet features to classify vitreous, starchy durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amount of features based on the feature rank list obtained with ANOVA test. The data we have used in this study was video images of wheat kernels and foreign objects present on a conveyor belt camera system with illumination provided by daylight colour powerleds. The maximum classification accuracy was 93.46% obtained with 210 feature neural network function which was generated and applied on the video containing a mixture of wheat kernels and foreign objects.