An intelligent system for sorting pistachio nut varieties
An intelligent pistachio nut sorting system combining acoustic emissions analysis, Principal Component Analysis (PCA) and Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested. To evaluate the performance of the system 3200 pistachio nuts from four native Iranian pistachio nut varieties were used. Each variety was consisted of 400 split-shells and 400 closed-shells nut. The nuts were randomly selected, slide down a chute, inclined 60 above the horizontal, on which nuts slide down to impact a steel plate and their acoustic signals were recorded from the impact. Sound signals in the time-domain are saved for subsequent analysis. The method is based on feature generation by Fast Fourier Transform (FFT), feature reduction by PCA and classification by MFNN. 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. To find the optimal MFNN classifier, various topologies each having different number of neurons in the hidden layer were designed and evaluated. The best MFNN model had a 40124 structure, that is, a network having one hidden layer with 40 neurons at its input, 12 neurons in the hidden layer and 4 neurons (pistachio varieties) in the output layer. The selection of the optimal model was based on the examination of mean square error, correlation coefficient and correct separation rate (CSR). The CSR or total weighted average in system accuracy for the 40124 structure was 97.5%, that is, only 2.5% of nuts were misclassified.
sorting pistachio nut principal component analysis neural network classification fast fourier transform impact acoustic