Metrics Comparison of Machine Learning Algorithms used to classify Noiler Chicken Egg from Egg QualityTrait

This study evaluated the predictive performance metrics of four machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Linear Regression (LRG)) for classifying egg size based on internal and external egg quality traits of Noiler chickens. Three hundred freshly laid eggs (100 per plumage variety) were collected at young laying age (26 weeks) and old laying age (46 weeks), and assessed for various quality parameters. External traits included egg weight, egg width, egg length, shell surface area, percentage of shell thickness, and shell weight) while internal traits included albumen height, haugh unit, yolk height, yolk index, yolk and albumen weight and yolk width. Data were analysed using Python-based implementations of the four algorithms. Among the models, the Random Forest algorithm achieved the highest classification accuracy (98%), with perfect precision (1.00) and a recall of 0.98 which indicated exceptional predictive ability. SVM and Logistic Regression both recorded accuracies of 95%, while linear regression recorded 92% Therefore, the model developed from the Random Forest algorithm can be effectively used for automated egg grading and selection in poultry breeding programs. Future research could incorporate additional features such as computer vision and deep learning techniques to further enhance prediction accuracy.