System for removing shell pieces from hazelnut kernels using impact vibration analysis

Abstract

a b s t r a c tA system for removing shell pieces from hazelnut kernels using impact vibration analysis was developed in which nuts are dropped onto a steel plate and the vibration signals are captured and analyzed. The mel-cepstral feature parameters, line spectral frequency values, and Fourier-domain Lebesgue features were extracted from the vibration signals. The best experimental results were obtained using the melcepstral feature parameters. The feature parameters were classified using a support vector machine (SVM), which was trained a priori using a manually classified dataset. An average recognition rate of 98.2% was achieved. An important feature of the method is that it is easily trainable, enabling it to be applicable to other nuts, including walnuts and pistachio nuts. In addition, the system can be implemented in real time.

A New EEMD-Based Scheme for Detection of Insect Damaged Wheat Kernels Using Impact Acoustics

“…After the work by Pearson et al, improvedp rocedures for sorting pistachios were studied [4,5,6,7,8]. In addition, detection techniques based on impact acoustics were used to separate fully developed from underdeveloped hazelnuts [9], separate undamaged from damaged/cracked hazelnut kernels [10], remove shell pieces from hazelnut kernels [11], dis- criminate between potato tubers and clods [12,13], and separate filled and empty walnuts [14].…”