Detection of Walnut Varieties Using Impact Acoustics and Artificial Neural Networks (ANNs)

Simin Khalesi, Asghar Mahmoudi, Adel Hosainpour, Aliakbar Alipour


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.

Full Text:



Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.