Grading of Iranian's Export Pistachio Nuts Based on Artificial Neural Networks

In this paper an intelligent separation system, based on artificial neural networks (ANNs), for classifying four different varieties of Iranian pistachio nuts, namely, Kaleghouchi (Ka), Akbari (Ak), Badami (Ba) and Ahmadagaee (Ah) is presented. To develop the ANN models a total of 3200 pistachio sound signals, 800 samples for each variety, were recorded. Features of pistachio nut varieties were extracted from analysis of sound signal in both time and frequency domains by means of Fast Fourier Transform, power spectral density and principal component analysis methods. Altogether forty features were selected as input vector to ANN models. Network output vector consisted of four neurons for classification of varieties. Collected data for 3200 pistachio nuts were divided into three sets: 70% (2240 data points in total) for training, 15% for testing, and the remaining 15% were used for cross validation of ANN models. In developing the ANN models, twelve ANN architectures, each having different numbers of neurons in hidden layer, were evaluated. The optimal model was selected after several evaluations based on minimizing of mean square error (MSE), correct separation rate (CSR) and coefficient of correlation. Selected optimal ANN for classification was of 40 – 12 4 configuration. CSR of proposed optimal ANN model for four pistachio varieties, Ka, Ak, Ba and Ah, were 96.97%, 97.64%, 96.36% and 99.10%, respectively. Net weight average of system accuracy was found to be 97.51%.