THE DETECTION OF EGGSHELL CRACKS USING DIFFERENT CLASSIFIERS

Abstract

Chicken eggs, which are widely consumed in daily life due to their rich nutritional values, are also used in many products. The increasing need for eggs must be met quickly for various circumstances. Eggs are subjected to various impacts and shaken from production to packaging. In some cases, these effects cause an eggshell to crack. While these cracks are sometimes visible, sometimes they are micro-sized and cannot be seen. The crack causes an egg to enter into harmful microorganisms and spoil in a short time. In this study, the acoustic signals generated in consequence of the application using a mechanical contact to eggs were recorded for the duration of 0.2 seconds at 50 kHz sampling frequency with the help of a microphone. To determine the active part in the collected acoustic signal data, a clipping process was implemented by a thresholding process. Thus, the exactly correct moment of mechanical contact on the eggshell was easily detected. Statistical parameters, which are min, max, difference, mean, standard deviation, skewness, and kurtosis values, are extracted from the thresholded data signals so that 7-dimensional feature vectors has been constituted. Finally, the Common Vector Approach (CVA) is applied on the extracted feature vectors, 100% success rate has been achieved for the test data set. The ANN and SVM classifiers in where the same feature vectors are treated were used for the comparison purpose, and exactly the same classification rates are attained; however, the less number of eggs are tested with the ANN and SVM classifiers in the same amount of time. With the proposed mechanical system and classification methodology, it takes about 0.2008 seconds to determine whether the shells of eggs are cracked/intact. Therefore, the proposed combination of the feature vectors based on statistical features and CVA as a classifier for the detection of cracks on eggshells is notably appropriate especially for industrial applications in terms of speed and accuracy aspects.