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Updated: Sep 11, 2026
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
Published on: August 22, 2018
Investigation of factors affecting egg breakage resistance in laying hens using data mining and machine learning
Şenol Çelik1, Turgay Şengül1, Ömer Şengül2
1Department of Animal Science, Agricultural Faculty, Bingöl University, Bingöl, Türkiye.
Introduction:
This study aims to evaluate the predictive performance of different machine learning algorithms and to identify the key factors influencing eggshell breaking resistance in laying hens.
Methods:
Three machine learning methods, C5.0 decision tree, Random Forest (RF), and Support Vector Regression (SVR), were applied to egg classification and prediction tasks. Eggshell strength (ER) was predicted using egg weight (EW), shell weight (SW), shell thickness (ST), and shape index (SI).
Results:
The C5.0 decision tree, trained on 464 eggs, achieved an overall classification accuracy of 65.1%, with a tendency to misclassify brown eggs as white, suggesting potential feature overlap or class imbalance. Among the regression models, RF outperformed SVR, yielding higher R2 (0.852 vs. 0.553) and adjusted R2 (0.835 vs. 0.548) values, along with lower error metrics (MSE, RMSE, and MAPE). In addition to superior predictive accuracy, the RF model provided insights into the relative importance of egg quality traits affecting eggshell breaking resistance.
Discussion:
Overall, the findings indicate that ensemble-based machine learning methods are effective tools for both accurate prediction and identification of influential factors related to eggshell strength.
