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Prediction of Egg Weight in Japanese Quails (Coturnix coturnix japonica) with Internal Quality Traits Using Machine Learning Algorithms

Year 2025, Volume: 28 Issue: 6, 1142 - 1152

Mehmet Eroğlu , Sultan Aslan

https://doi.org/10.18016/ksutarimdoga.vi.1695149

Abstract

This study comparatively evaluates the performance of various machine learning (ML) algorithms to predict quail egg weight based on internal quality characteristics. Two hundred Japanese quail eggs were used, and measurements were taken for albumen length, height, and width, as well as yolk diameter, albumen index, yolk index, and Haugh unit. Linear Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting, and XGBoost algorithms were applied for egg weight prediction, with hyperparameter optimization performed using the GridSearchCV method. Performance was assessed using R², RMSE, MAE, MAPE, RAE, and MAD metrics. SVM and Linear Regression models demonstrated high generalization ability by providing balanced results between training and test datasets. The SVM and Linear Regression achieved the highest R² (0.990) and the lowest error values on the test data, making them the most successful algorithms. In contrast, XGBoost, Gradient Boosting, and Random Forest models showed high accuracy on training data, but experienced noticeable performance drops on test data, indicating overfitting issues. As a result, the SVM and linear regression models stand out as practical, reliable, and applicable methods in innovative farming applications aimed at increasing production efficiency, optimizing resource use, and establishing early decision support systems

Keywords

Machine learning Egg weight Artificial intelligence Quail Internal quality traits

References

  • Arthur, J., & Bejaei, M. (2017). Quail eggs. In Egg innovations and strategies for improvements (pp. 13-21). Academic Press.
  • Brasil, Y. L., Cruz-Tirado, J. P., & Barbin, D. F. (2022). Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food control, 131, 108418.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
  • Çelik, Ş., Eyduran, E., Şengül, A. Y., & Şengül, T. (2021). Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms. Tropical Animal Health and Production, 53(3), 382.
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250.
  • Chen, J. T., He, P. G., Jiang, J. S., Yang, Y. F., Wang, S. Y., Pan, C. H., ... & Pan, J. M. (2023). In vivo prediction of abdominal fat and brefast muscle in broiler chicken using live body measurements based on machine learning. Poultry Science, 102(1), 102239.
  • Çimrin, T., & Tunca, R. İ. (2012). Bıldırcın beslemede alternatif yem ve katkıların kullanımı. Journal of the Institute of Science and Technology, 2(3), 109-116.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
  • De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48.
  • Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.
  • Faraz, A., Tırınk, C., Önder, H., Şen, U., Ishaq, H. M., Tauqir, N. A., ... & Nabeel, M. S. (2023). Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed. Tropical Animal Health and Production, 55(4), 276.
  • Freeman, E. A., Moisen, G. G., Coulston, J. W., & Wilson, B. T. (2016). Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, 46(3), 323-339.
  • Gammermann, A. (2000). Support vector machine learning algorithm and transduction. Computational Statistics, 15(1), 31-39.
  • Gao, Z., Zheng, J., & Xu, G. (2025). Research Progress and Technological Application Prospects of Comprehensive Evaluation Methods for Egg Freshness. Foods, 14(9), 1507.
  • Gonzalez-Mora, A. F., Rousseau, A. N., Larios, A. D., Godbout, S., & Fournel, S. (2022). Assessing environmental control strategies in cage-free aviary housing systems: Egg production analysis and Random Forest modeling. Computers and Electronics in Agriculture, 196, 106854.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
  • Haugh, R. R. (1937). The Haugh unit for measuring egg quality. United States Egg Poultry Magazine, 43, 552-555.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, No. 1). Springer.
  • Ji, H., Xu, Y., & Teng, G. (2025). Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations. Poultry Science, 104(1), 104458.
  • Jun, M. J. (2021). A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area. International Journal of Geographical Information Science, 35(11), 2149-2167.
  • Kim, S. J., Bae, S. J., & Jang, M. W. (2022). Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability, 14(18), 11674.
  • Knights, V., & Prchkovska, M. (2024). From equations to predictions: Understanding the mathematics and machine learning of multiple linear regression. Journal of Mathematical & Computer Applications, 3(2), 1-8.
  • Lever, J., Krzywinski, M., & Altman, N. (2016). Points of significance: model selection and overfitting. Nature Methods, 13(9), 703-705.
  • Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766.
  • Moran Jr, E. T. (2007). Nutrition of the developing embryo and hatchling. Poultry science, 86(5), 1043-1049.
  • Namlı, A., & Ölçer, D. (2024). Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 27(Ek Sayı 1 (Suppl 1)), 243-253.
  • Narushin, V. G., & Romanov, M. N. (2002). Egg physical characteristics and hatchability. World's Poultry Science Journal, 58(3), 297-303.
  • Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, 107266.
  • Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019). Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, 161, 131-140.
  • Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
  • Sehirli, E., & 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, 205, 117692.
  • Shahinfar, S., Kelman, K., & Kahn, L. (2019). Prediction of sheep carcass traits from early-life records using machine learning. Computers and electronics in agriculture, 156, 159-177.
  • Tyasi, T. L., & Celik, S. (2024). Investigation of Egg Quality Characteristics Affecting Egg Weight of Lohmann Brown Hen with Data Mining Methods. Poultry Science Journal, 12(1), 107-117.
  • Urooj, M., & Iqbal, F. (2023). An Ensemble Machine Learning Approach For The Prediction Of Body Weight Of Chickens From Body Measurement. Journal of Animal & Plant Sciences, 33(4), 794-804.
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
  • Yıldız, B. İ., & Karabağ, K. (2025). Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 28(1), 247-255.
  • Yıldız, B. İ., Eskioğlu, K., & Karabağ, K. (2024). Developing a machine learning prediction model for honey production. Mediterranean Agricultural Sciences, 37(2), 105-110.
  • Zhang, F., & O’Donnell, L. J. (2020). Support vector regression. In Machine learning (pp. 123–140). Elsevier.

Makine Öğrenmesi Algoritmalarıyla Japon Bıldırcınlarda (Coturnix coturnix japonica) Yumurta Ağırlığının İç Kalite Özellikleri ile Tahmin Edilmesi

Year 2025, Volume: 28 Issue: 6, 1142 - 1152

Mehmet Eroğlu , Sultan Aslan

https://doi.org/10.18016/ksutarimdoga.vi.1695149

Abstract

Bu çalışma, bıldırcın yumurta ağırlığını iç kalite özelliklerine dayanarak tahmin etmek amacıyla çeşitli makine öğrenmesi (ML) algoritmalarının performansını karşılaştırmalı olarak değerlendirmektir. Toplam 200 Japon bıldırcın yumurtası kullanılarak albümin uzunluğu, yüksekliği ve genişliği ile sarı çapı, albümin indeksi, sarı indeks ve Haugh birimi belirlenmiştir. Yumurta ağırlığı tahmini için Doğrusal Regresyon, Destek Vektör Makineleri (SVM), Random Forest, Gradient Boosting ve XGBoost algoritmaları uygulanmış, hiperparametre optimizasyonu GridSearchCV yöntemiyle gerçekleştirilmiştir. Performans değerlendirmesi için R², RMSE, MAE, MAPE, RAE ve MAD gibi ölçütler kullanılmıştır. SVM ve Lineer Regresyon modelleri, eğitim ve test verileri arasında dengeli sonuçlar sunarak yüksek genelleme kabiliyeti göstermiştir. SVM ve Doğrusal Regresyon algoritmaları test verisinde en yüksek R² (0.990) ve en düşük hata değerlerini elde ederek en başarılı modeller olmuştur. Buna karşılık, XGBoost, Gradient Boosting ve Random Forest modelleri eğitim verilerinde yüksek başarı sağlasa da test verilerinde belirgin performans düşüşü göstererek aşırı öğrenme sorunu göstermiştir. Sonuç olarak SVM ve Doğrusal Regresyon modelleri, üretim verimliliğini artırmayı, kaynak kullanımını optimize etmeyi ve erken karar destek sistemleri oluşturmayı hedefleyen akıllı tarım uygulamalarında doğruluk ve genelleme açısından etkili, güvenilir ve uygulanabilir yöntemler olarak öne çıkmaktadır.

Keywords

Makine öğrenmesi Yumurta ağırlığı Yapay zeka Bıldırcın İç kalite özellikleri

References

  • Arthur, J., & Bejaei, M. (2017). Quail eggs. In Egg innovations and strategies for improvements (pp. 13-21). Academic Press.
  • Brasil, Y. L., Cruz-Tirado, J. P., & Barbin, D. F. (2022). Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food control, 131, 108418.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
  • Çelik, Ş., Eyduran, E., Şengül, A. Y., & Şengül, T. (2021). Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms. Tropical Animal Health and Production, 53(3), 382.
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250.
  • Chen, J. T., He, P. G., Jiang, J. S., Yang, Y. F., Wang, S. Y., Pan, C. H., ... & Pan, J. M. (2023). In vivo prediction of abdominal fat and brefast muscle in broiler chicken using live body measurements based on machine learning. Poultry Science, 102(1), 102239.
  • Çimrin, T., & Tunca, R. İ. (2012). Bıldırcın beslemede alternatif yem ve katkıların kullanımı. Journal of the Institute of Science and Technology, 2(3), 109-116.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
  • De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48.
  • Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.
  • Faraz, A., Tırınk, C., Önder, H., Şen, U., Ishaq, H. M., Tauqir, N. A., ... & Nabeel, M. S. (2023). Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed. Tropical Animal Health and Production, 55(4), 276.
  • Freeman, E. A., Moisen, G. G., Coulston, J. W., & Wilson, B. T. (2016). Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, 46(3), 323-339.
  • Gammermann, A. (2000). Support vector machine learning algorithm and transduction. Computational Statistics, 15(1), 31-39.
  • Gao, Z., Zheng, J., & Xu, G. (2025). Research Progress and Technological Application Prospects of Comprehensive Evaluation Methods for Egg Freshness. Foods, 14(9), 1507.
  • Gonzalez-Mora, A. F., Rousseau, A. N., Larios, A. D., Godbout, S., & Fournel, S. (2022). Assessing environmental control strategies in cage-free aviary housing systems: Egg production analysis and Random Forest modeling. Computers and Electronics in Agriculture, 196, 106854.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
  • Haugh, R. R. (1937). The Haugh unit for measuring egg quality. United States Egg Poultry Magazine, 43, 552-555.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, No. 1). Springer.
  • Ji, H., Xu, Y., & Teng, G. (2025). Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations. Poultry Science, 104(1), 104458.
  • Jun, M. J. (2021). A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area. International Journal of Geographical Information Science, 35(11), 2149-2167.
  • Kim, S. J., Bae, S. J., & Jang, M. W. (2022). Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability, 14(18), 11674.
  • Knights, V., & Prchkovska, M. (2024). From equations to predictions: Understanding the mathematics and machine learning of multiple linear regression. Journal of Mathematical & Computer Applications, 3(2), 1-8.
  • Lever, J., Krzywinski, M., & Altman, N. (2016). Points of significance: model selection and overfitting. Nature Methods, 13(9), 703-705.
  • Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766.
  • Moran Jr, E. T. (2007). Nutrition of the developing embryo and hatchling. Poultry science, 86(5), 1043-1049.
  • Namlı, A., & Ölçer, D. (2024). Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 27(Ek Sayı 1 (Suppl 1)), 243-253.
  • Narushin, V. G., & Romanov, M. N. (2002). Egg physical characteristics and hatchability. World's Poultry Science Journal, 58(3), 297-303.
  • Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, 107266.
  • Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019). Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, 161, 131-140.
  • Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
  • Sehirli, E., & 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, 205, 117692.
  • Shahinfar, S., Kelman, K., & Kahn, L. (2019). Prediction of sheep carcass traits from early-life records using machine learning. Computers and electronics in agriculture, 156, 159-177.
  • Tyasi, T. L., & Celik, S. (2024). Investigation of Egg Quality Characteristics Affecting Egg Weight of Lohmann Brown Hen with Data Mining Methods. Poultry Science Journal, 12(1), 107-117.
  • Urooj, M., & Iqbal, F. (2023). An Ensemble Machine Learning Approach For The Prediction Of Body Weight Of Chickens From Body Measurement. Journal of Animal & Plant Sciences, 33(4), 794-804.
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
  • Yıldız, B. İ., & Karabağ, K. (2025). Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 28(1), 247-255.
  • Yıldız, B. İ., Eskioğlu, K., & Karabağ, K. (2024). Developing a machine learning prediction model for honey production. Mediterranean Agricultural Sciences, 37(2), 105-110.
  • Zhang, F., & O’Donnell, L. J. (2020). Support vector regression. In Machine learning (pp. 123–140). Elsevier.
There are 38 citations in total.

Details

Primary Language English
Subjects Animal Science, Genetics and Biostatistics
Journal Section RESEARCH ARTICLE
Authors

Mehmet Eroğlu 0000-0001-9471-6410

Sultan Aslan 0000-0001-8480-1515

Early Pub Date August 15, 2025
Publication Date September 5, 2025
Submission Date May 8, 2025
Acceptance Date August 8, 2025
Published in Issue Year 2025 Volume: 28 Issue: 6

Cite

APA Eroğlu, M., & Aslan, S. (2025). Prediction of Egg Weight in Japanese Quails (Coturnix coturnix japonica) with Internal Quality Traits Using Machine Learning Algorithms. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 28(6), 1142-1152. https://doi.org/10.18016/ksutarimdoga.vi.1695149
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