Abstract
Hyperspectral imaging (HSI) is a smart, non-destructive sensing that integrates spectral and spatial information, advancing fertility prediction and structural evaluation of eggs. Despite increasing interest in poultry research, there remains no publicly accessible HSI dataset, limiting the development and validation of robust, generalizable machine learning models. To address this gap, the present study provides a comprehensive HSI dataset encompassing key egg parameters, pre-incubation fertility status, eggshell thickness, yolk mass, eggshell strength, and associated morphological parameters (intact egg mass, major and minor diameters). The dataset consists of hyperspectral images of 1228 white Leghorn chicken eggs acquired using a line-scan transmission HSI system (374–1015 nm). Each egg is accompanied by validated reference measurements, enabling supervised learning tasks such as regression and classification. Raw hyperspectral cubes (.mat format) and annotated spectral metadata (.csv format) are structured for easy access and reuse. Rigorous technical validation confirmed the dataset’s reliability. This open-access resource is designed to accelerate precision poultry research and promote the development of non-invasive, data-driven egg evaluation and quality assurance techniques.
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Data availability
The hyperspectral egg dataset reported in this study is publicly available in the Illinois Data Bank at https://doi.org/10.13012/B2IDB-8141497_V1. The repository includes three files: egg_hyperspectral_image_readme, all_data.rar, and spectra and reference parameters.csv. The readme file provides detailed instructions and descriptions of the data structure. The all_data.rar file contains raw hyperspectral images in.mat format, while the spectra and reference parameters.csv file includes corresponding spectral data and reference parameters.
Code availability
This study utilized Python code on Google Colab for all data processing and visualization, allowing users to run the code without installing Python software. All Python codes used in this study are publicly available on GitHub at the following link: https://github.com/dexter1d/Egg_data_process. The repository includes scripts for spectral visualization, correlation analysis, data partitioning, spectral pre-processing, and model development. Each section of the code is accompanied by detailed instructions and comments to guide users and ensure the reproducibility of the results reported in this study. In addition to the instructions in the code files, users can refer to the ‘user guides.pdf’ for detailed guidance on using the dataset and replicating the model performances reported in this study.
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Acknowledgements
This work was supported by the USDA National Institute of Food and Agriculture, Award # 2023-67015-39154.
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M.W.A. wrote the manuscript, collected reference data, and analyzed the data reported in this study. D.S. acquired hyperspectral images and performed data curation. M.T.A. collected reference data and performed data curation. M.K. designed the study and developed the overall experimental plan. All authors reviewed and approved the final manuscript.
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Ahmed, M.W., Song, D., Ahmed, M.T. et al. Hyperspectral imaging dataset for non-destructive fertility and structural evaluation of chicken eggs. Sci Data (2026). https://doi.org/10.1038/s41597-026-06556-1
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DOI: https://doi.org/10.1038/s41597-026-06556-1
