Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning

Abstract

The egg industry heavily relies on accurate detection of egg fertility to optimize hatchery operations. Conventional methods, such as candling, rely on human interpretation, which is labor-intensive, time-consuming, and thus not efficient in large-scale operations. This study developed a fast, accurate, and non-destructive method of pre-incubated chicken egg fertility detection using hyperspectral imaging (HSI) and machine learning. The Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF), and Support Vector Machine (SVM) calibration models were developed at full wavelengths (374–1015 nm), and the performance of the models was evaluated by 10-fold cross-validation and independent validation. Different spectral pre-processing and important feature selection methods were assessed for robust prediction model development. In addition to raw or non-synthetic data, synthetic data is also used to develop classification models. Using full wavelengths, the CatBoost model with synthetic data showed the best classification performance, attaining 95.1% accuracy in independent validation. The CatBoost models with fewer important features showed good prediction performance, making them computationally efficient, robust, and interpretable. The Shapley explainable artificial intelligence (AI) method was used to interpret the robust CatBoost model, revealing that wavelength regions associated with yolk color, pre-incubation cellular activities related to embryonic development, changes in hydration levels, and variations in protein and lipid contents between fertile and infertile eggs are crucial for pre-incubation chicken egg fertility classification. This study highlighted the efficacy of HSI combined with machine learning as a potential green technology for egg fertility detection towards a sustainable egg industry.

Topics & Concepts