Hyperspectral Imaging Combined with a Deep Residual Network for a Precision Nutrition Prediction Model in Commercial Laying Hens

The aim of this study was to develop a precision nutrition prediction model for commercial laying hens based on hyperspectral imaging technology and a deep residual network. Eggs from a large‑scale laying hen farm (20,000 birds) were used as research subjects. Hyperspectral image data in the 400–1000 nm range were collected, and the contents of crude protein, crude fat, lecithin, and cholesterol were determined by conventional chemical analysis. An improved deep residual network (ResNet‑SP) was proposed for spectral feature extraction and nutritional index prediction, and its performance was compared with partial least squares regression (PLSR), support vector machine (SVM), and the conventional residual network (ResNet50). The results showed that the ResNet‑SP model achieved the best prediction performance for the four nutritional indices. For crude protein content, the coefficient of determination for the prediction set (R²_p) was 0.931, the root mean square error of prediction (RMSEP) was 0.87 g/100 g, and the residual predictive deviation (RPD) was 3.82. This study verifies the feasibility of combining hyperspectral imaging with deep residual networks for non‑destructive detection of nutritional quality in eggs, providing technical support for precision nutrition management in large‑scale farms.

References

Lindley ST, 2003, Estimation of Population Growth and Extinction Parameters from Noisy Data. Ecological Applications, 2003(13): 806–813.

Martin H, 1994, The Archaean Grey Gneisses and the Genesis of Continental Crust, in Archaean Crustal Evolution, Elsevier, Amsterdam, 205–259.

Adegbenjo AO, Liu L, Ngadi MO, 2024, An Adaptive Partial Least-squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging. Sensors, 24(5): 1485. https://doi.org/10.3390/s24051485

Lin H, He X, Chen H, et al., 2022, A Residual Dense Comprehensively Regulated Convolutional Neural Network to Identify Spectral Information for Egg Quality Traceability. Analytical Methods, 2022(14): 3780–3789. https://doi.org/10.1039/D2AY01371A

Ahmed MW, Emmert JL, Kamruzzaman M, 2025, Prediction of Egg Albumen Content using Hyperspectral Imaging and Machine Learning. 2025 ASABE Annual International Meeting, 2025, Paper No. 2500802. https://doi.org/10.13031/aim.202500802

Chen SY, Hsu SH, Ko CY, et al., 2023, Real-time Defect and Freshness Inspection on Chicken Eggs using Hyperspectral Imaging. Food Control, 2023(150): 109716. https://doi.org/10.1016/j.foodcont.2023.109716

Ahmed MW, Hossainy SJ, Khaliduzzaman A, et al., 2023, Non-destructive Optical Sensing Technologies for Advancing the Egg Industry toward Industry 4.0: A Review. Comprehensive Reviews in Food Science and Food Safety, 22(6): 4378–4403. https://doi.org/10.1111/1541-4337.13227

Cai M, Li X, Liang J, et al., 2024, An Effective Deep Learning Fusion Method for Predicting the TVB-N and TVC Contents of Chicken Breasts using Dual Hyperspectral Imaging Systems. Food Chemistry, 2024(445): 139847. https://doi.org/10.1016/j.foodchem.2024.139847

Ahmed MW, Khaliduzzaman A, Emmert JL, et al., 2025, An Overview of Recent Advancements in Hyperspectral Imaging in the Egg and Hatchery Industry. Computers and Electronics in Agriculture, 2025(230): 109796. https://doi.org/10.1016/j.compag.2024.109796

He K, Zhang X, Ren S, et al., 2016, Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, 770–778. https://doi.org/10.1109/CVPR.2016.90

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