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. 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.