Performance of Classification Models in Japanese Quail Egg Sexing
Jesusimo L. Dioses, Ruji P. Medina, Arnel C. Fajardo, Alexander A. Hernandez
Abstract
The method of identifying the gender of an avian egg before hatching, egg sexing, has been one of the interesting fields of research in poultry and egg industries to improve its production with reduced costs. Researchers started to study and suggested various scientific methods to determine the sex of avian eggs like chicken and duck. The study proposed the extraction of seven (7) Japanese quail egg morphology features using image processing techniques and edge detection models. Kernel Naïve Bayes, Logistic Regression, and Quadratic SVM models tested and validated Japanese quail eggs' extracted morphology data to classify their sexes. Confusion matrices were used to determine the male, female and average sex classification accuracy rate of each model. Results show that two (2) morphology features of the Japanese quail egg, such as eccentricity and shape index, can be used as significant factors in classifying its sexes. Gaussian Naïve Bayes model is the best classifier to test and validate the morphology characteristics and data of Japanese quail eggs. It has a classification rate of 85.14% for males, 80.16% for females, and an average of 82.88% for both sexes.