Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning

Abstract

Non-destructive sex determination in eggs can enhance animal welfare, improve economic efficiency, reduce environmental impact, and foster technological innovation in sustainable hatchery operations. This study investigates the effectiveness of non-destructive hyperspectral imaging (HSI) and machine learning for pre-incubation sex prediction in chicken eggs. Multiple classification models such as partial least squares discriminant analysis (PLS-DA), Extreme Gradient Boosting (XGBoost), random forest (RF), and Categorical Boosting (CatBoost) were developed across full wavelengths (452–899 nm) and evaluated through external validation. Multiple spectral pre-processing, such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) were assessed for calibration model development. Further, important feature selection and model optimization techniques were evaluated for robust prediction model development. Using 35 important features, the CatBoost model with SG pre-processed spectra achieved the best performance, with an accuracy of 82.9% on the calibration set and 75.5% on the validation set. The study demonstrated the potential of HSI and advanced machine learning to revolutionize sex prediction in chicken eggs before incubation, offering a non-invasive, precise, and efficient solution for the next-generation poultry industry. • Hyperspectral imaging was applied to predict pre-incubated egg sex. • Data splitting, spectral pre-processing, and feature selection methods were explored. • The CatBoost model showed the highest validation accuracy (83%). • Synthetic data further ensures the robustness of the prediction model.

Topics & Concepts