Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters

Abstract

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results.