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

Mehmet Eroğlu; Sultan Aslan

doi:10.18016/ksutarimdoga.vi.1695149

EN TR

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

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

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

Öz

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.

Anahtar Kelimeler

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Details

Primary Language

English

Subjects

Animal Science, Genetics and Biostatistics

Journal Section

Research Article

Authors

Mehmet Eroğlu ^*
0000-0001-9471-6410
Türkiye

Sultan Aslan
0000-0001-8480-1515
Türkiye

Early Pub Date

August 15, 2025

Publication Date

October 20, 2025

Submission Date

May 8, 2025

Acceptance Date

August 8, 2025

Published in Issue

Year 2025 Volume: 28 Number: 6

DOI

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

IZ

https://izlik.org/JA77SY92DB

Cite

RIS / Bibtex

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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