Predicting Quail Egg Quality Using Machine Learning Algorithms

ABSTRACT This study evaluates the effectiveness of machine learning algorithms in predicting quail egg quality based on nine key parameters, including egg weight, egg width, egg length, yolk height, yolk width, yolk weight, albumen height, albumen width, and albumen length. A dataset comprising 350 eggs from 18-week-old Japanese quails was analyzed using Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting. The findings revealed that models combining internal and external quality parameters achieved significantly higher accuracy compared to models based solely on external attributes. Notably, Random Forest and Gradient Boosting algorithms achieved accuracies exceeding 97%, while predictions based only on external parameters exhibited lower accuracy but presented a promising starting point for non-invasive evaluations. This study strongly highlights the applicability and flexibility of machine learning in evaluating quail egg quality. The ability of algorithms to integrate various quality parameters and analyze complex relationships provides both rapid and scalable solutions. These findings demonstrate that machine learning technologies have the potential to drive innovative approaches in the poultry industry and inspire future research focusing on larger datasets and additional parameters to further enhance accuracy.