Journal of Food, Agriculture and Environment

An intelligent system for grading walnuts based on acoustic emission and neural networks

Author(s):

Smail Khalifa ^1*, Mohammad Hassan Komarizadeh ¹, Behrouz Tousi ²,  Ali Mohammad Nikbakht ¹,

Recieved Date: 2010-11-17, Accepted Date: 2011-01-11

Abstract:

In this paper an intelligent walnut recognition system combining acoustic emissions analysis and Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested for classification of walnuts in three classes. To evaluate the performance of the system, 326 samples were used. Walnuts were divided into empty, average and fully developed categories based on their fullness. In order to produce sound signals, a 60° inclined polished steel plate with 150 mm diameter was used. Amplitude of time domain, Fast Fourier Transform (FFT), Phase and Power Spectral Density (PSD) of impact signals were measured. Features of phase and power spectrum of sound signals are computed via a 1024-point FFT, while amplitude is directly received from impact signal. After feature generation, 60% of samples were used for training, 20% for validation, and remaining samples were selected for testing. The optimized MFNN model was found to have a 326–12–4 architecture. The selection of the optimal model was based on the evaluation of mean square error and correct separation rate (CSR). The CSR percentages for fully developed, average and empty walnuts were obtained to be 97.62, 80.00 and 93.33, respectively. Total weighted average in system accuracy for the 326–12– 4 structure was 95.38%, i.e. only 4.62% of walnuts were misclassified. Considering the overall aspects of the results, it can be stated that the developed system was successful enough to correlate the acoustic features with fullness of walnut fruit.

Keywords:

Walnut, acoustic, signal processing, PSD, neural networks

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Journal: Journal of Food, Agriculture and Environment
Year: 2011
Volume: 9
Issue: 1
Category: Food and Health
Pages: 109-112

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