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

• We create a system for removing shell pieces from hazelnut kernels.
• This system can be used in food industry to recognize shell pieces automatically.
• Mel-cepstral parameters, LSF values, Lebesgue features, CHOG and FAB were used.
• Feature parameters were classified using an SVM with RBF and linear kernels.
• The system is easily trainable and can be implemented in real time.

A 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 mel-cepstral 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.