Eggshell Crack Detection and Egg Classification Using Resonance and Support Vector Machine Methods

Abstract

Abstract. Cracks in eggshells not only affect the egg preservation time but also reduce the success rate for the end-processed products. This study was based on the theory of resonant inspection (RI). The use of the support vector machine (SVM) method as a means of more accurate eggshell crack detection was evaluated. The results revealed that comparing the resonant frequency and amplitude by using a microphone as a sensor allowed non-cracked eggs to be distinguished from cracked eggs. The characteristic frequency of a non-cracked egg was between 4130 and 5500 Hz, and its amplitude was between 0.16 and 0.20 V. The spectrum of a cracked egg was fuzzy, with no obvious characteristic frequency, and the maximum amplitude was approximately 0.06 V. The identification accuracy was 99% and 98% for the SVM training set and testing set, respectively. These results prove that the resonance detection method is effective for identifying eggs with cracked shells. Keywords: Eggshells, Resonant inspection, Fast Fourier transform, Support vector machine.