EggQuality-UFRPE: A Chicken Egg Dataset for Multidisciplinary Studies in Animal Science and Computing
Resumo
This paper presents EggQuality-UFRPE, a multidisciplinary dataset comprising 2,150 chicken egg instances with 29 features, collected through manual measurements using advanced instrumentation. It addresses the scarcity of open-access empirical data in animal science, supporting multidisciplinary research with computer disciplines, such as data science and machine learning. A descriptive analysis is included, reporting mean, median, dispersion metrics, missing data proportions, and Pearson correlation coefficients among features. These statistics reveal the dataset’s internal consistency and support exploratory modeling. Despite limitations, such as a single hen strain, the dataset enables applications ranging from regression and predictive modeling to synthetic data generation and multimodal learning, promoting integration between computational and animal sciences.
Palavras-chave:
dataset, chicken egg, poultry, machine learning, animal science
Referências
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Coronel-Reyes, J., Ramirez-Morales, I., Fernandez-Blanco, E., Rivero, D. and Pazos, A. (2018). Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques. Computers and Electronics in Agriculture, v. 145, p. 1–10.
Duman, M., İekeroğlu, A., Yıldırım, A., Eleroğlu, H. and Camcı, Ö. (2016). Relation between egg shape index and egg quality characteristics. European Poultry Science, v. 80, p. 1–9.
Kakimoto, G. K., Haddadi, S. J., Araújo, P. M., et al. (2024). B2T: A Dataset of Tweets in Portuguese Language about Brazilian Banks. In Anais do VI Dataset Showcase Workshop (DSW 2024). . Sociedade Brasileira de Computação.
Anderson, K. E., Tharrington, J. B., Curtis, P. A. and Jones, F. T. (2003). Shell Characteristics of Eggs from Historic Strains of Single Comb White Leghorn Chickens and the Relationship of Egg Shape to Shell Strength. International Journal of Poultry Science, v. 3, n. 1, p. 17–19.
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López Vargas, J. P. B., De Abreu, K. L., Duarte de Paula, D., et al. (2024). Alternative Non-Destructive Approach for Estimating Morphometric Measurements of Chicken Eggs from Tomographic Images with Computer Vision. Foods, v. 13, n. 24, p. 4039.
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Silva, M. de L. M., Mendonça, A. L. C., D. Neto, E. R., et al. (2024). FACTO Dataset: A Dataset of User Reports for Faulty Computer Components. In Anais do VI Dataset Showcase Workshop (DSW 2024). . Sociedade Brasileira de Computação.
Soltani, M. and Omid, M. (2015). Detection of poultry egg freshness by dielectric spectroscopy and machine learning techniques. LWT - Food Science and Technology, v. 62, n. 2, p. 1034–1042.
Wang, D.-H., Chen, H., Zhou, R.-Y., et al. (2019). Study of measurement methods on phenotype of translucent eggs. Poultry Science, v. 98, n. 12, p. 6677–6683.
Zhang, W., Pan, L., Tu, S., Zhan, G. and Tu, K. (2015). Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. Journal of Food Engineering, v. 157, p. 41–48.
Zhao, J., Lin, H., Chen, Q., et al. (2010). Identification of egg’s freshness using NIR and support vector data description. Journal of Food Engineering, v. 98, n. 4, p. 408–414.
Coronel-Reyes, J., Ramirez-Morales, I., Fernandez-Blanco, E., Rivero, D. and Pazos, A. (2018). Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques. Computers and Electronics in Agriculture, v. 145, p. 1–10.
Duman, M., İekeroğlu, A., Yıldırım, A., Eleroğlu, H. and Camcı, Ö. (2016). Relation between egg shape index and egg quality characteristics. European Poultry Science, v. 80, p. 1–9.
Kakimoto, G. K., Haddadi, S. J., Araújo, P. M., et al. (2024). B2T: A Dataset of Tweets in Portuguese Language about Brazilian Banks. In Anais do VI Dataset Showcase Workshop (DSW 2024). . Sociedade Brasileira de Computação.
Anderson, K. E., Tharrington, J. B., Curtis, P. A. and Jones, F. T. (2003). Shell Characteristics of Eggs from Historic Strains of Single Comb White Leghorn Chickens and the Relationship of Egg Shape to Shell Strength. International Journal of Poultry Science, v. 3, n. 1, p. 17–19.
Lima, J., Santana, M., Correa, A. and Brito, K. (2023). The use and impact of TikTok in the 2022 Brazilian presidential election. In Proceedings of the 24th Annual International Conference on Digital Government Research. . ACM.
López Vargas, J. P. B., De Abreu, K. L., Duarte de Paula, D., et al. (2024). Alternative Non-Destructive Approach for Estimating Morphometric Measurements of Chicken Eggs from Tomographic Images with Computer Vision. Foods, v. 13, n. 24, p. 4039.
Narushin, V. G. (2005). Egg geometry calculation using the measurements of length and breadth. Poultry Science, v. 84, n. 3, p. 482–484.
Sehirli, E. and Arslan, K. (2022). An application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning models. Expert Systems with Applications, v. 205, p. 117692.
Silva, M. de L. M., Mendonça, A. L. C., D. Neto, E. R., et al. (2024). FACTO Dataset: A Dataset of User Reports for Faulty Computer Components. In Anais do VI Dataset Showcase Workshop (DSW 2024). . Sociedade Brasileira de Computação.
Soltani, M. and Omid, M. (2015). Detection of poultry egg freshness by dielectric spectroscopy and machine learning techniques. LWT - Food Science and Technology, v. 62, n. 2, p. 1034–1042.
Wang, D.-H., Chen, H., Zhou, R.-Y., et al. (2019). Study of measurement methods on phenotype of translucent eggs. Poultry Science, v. 98, n. 12, p. 6677–6683.
Zhang, W., Pan, L., Tu, S., Zhan, G. and Tu, K. (2015). Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. Journal of Food Engineering, v. 157, p. 41–48.
Zhao, J., Lin, H., Chen, Q., et al. (2010). Identification of egg’s freshness using NIR and support vector data description. Journal of Food Engineering, v. 98, n. 4, p. 408–414.
Publicado
29/09/2025
Como Citar
ABREU, Katariny Lima de; SOUZA, Andréa; CARMO, Matheus; SOUZA, Lilian Francisco de; RABELLO, Carlos Bôa-Viagem; SALVADEO, Denis; LIMA, João Paulo; BRITO, Kellyton.
EggQuality-UFRPE: A Chicken Egg Dataset for Multidisciplinary Studies in Animal Science and Computing. In: DATASET SHOWCASE WORKSHOP (DSW), 7. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 12-21.
DOI: https://doi.org/10.5753/dsw.2025.248204.
