Detection of walnut varieties using impact acoustics and artificial neural networks (ANNs)

Abstract

In this study, an acoustic-based intelligent system was developed for classifying of sangi and kaghazi genotypes of Iranian Walnuts. To develop the ANN models a total of 4000 Walnut sound signals, 2000 samples for each genotypes, were recorded. The nuts were randomly selected, slide down a chute, inclined 30 above the horizontal, on which nuts slide down to impact a steel plate and their acoustic signals were recorded from the impact. The resulting acoustic signals, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. The method is based on feature generation by Fast Fourier Transform (FFT), feature reduction by PCA and classification by Multilayer Feedforward Neural Network. Features such as amplitude, phase and power spectrum of sound signals are computed via a 1024-point FFT. By using PCA more than 98% reduction in the dimension of feature vector is achieved. In developing the ANN models, several ANN architectures, each having different numbers of neurons in hidden layer, were evaluated. The optimal model was selected after several evaluations based on minimizing the mean square error (MSE), correct detection rate (CDR) and correlation coefficient (r). Selected ANN for classification was of 47-18-2 configuration. CDR of the proposed ANN model for two walnut genotypes, Sangi and Kaghazi were 99.64 and 96.56 respectively. MSE of the system was found to be 0.0185.