Document Type : Original Article

Abstract

This study was conducted to achieve effective and low-cost technology for non-destructive grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic system composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic controller unit was constructed and tested. To evaluate system operation according to almond variety and class (hard, semi-soft, and soft), the effect of an acoustic signal generated by dropping the nuts onto a steel plate was captured by microphone and the amplitude, phase, and power spectral density were extracted from analysis of the sound signal in the time and frequency domains by means of fast Fourier transform. A multilayer perceptron neural network with a LM training function were used in all experiments. The classification accuracy using validation data was about 96.2% in the offline mode, but accuracy decreased to 88% in the online mode. This decrease in accuracy was probably the result of a difference in size and mass of the almond samples in the hard and semi-soft classes.

Keywords

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