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.

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Zootekni, Genetik ve Biyoistatistik

Bölüm

Araştırma Makalesi

Yazarlar

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

Sultan Aslan
0000-0001-8480-1515
Türkiye

Erken Görünüm Tarihi

15 Ağustos 2025

Yayımlanma Tarihi

20 Ekim 2025

Gönderilme Tarihi

8 Mayıs 2025

Kabul Tarihi

8 Ağustos 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 6

DOI

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

IZ

https://izlik.org/JA77SY92DB

Kaynak Göster

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

Cited By

Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach

Tropical Animal Health and Production

https://doi.org/10.1007/s11250-025-04822-9

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

Abstract

Keywords

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach

e-ISSN: 2619-9149