Prediction of Daily Egg-Laying in Japanese Quails Using Machine Learning

Creative Commons License

Eskioğlu K., Yıldız B. İ., ÖZDEMİR D., Akşit M.

Kafkas Universitesi Veteriner Fakultesi Dergisi, cilt.32, sa.3, ss.395-399, 2026 (SCI-Expanded, Scopus, TRDizin)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.9775/kvfd.2026.36392
  • Dergi Adı: Kafkas Universitesi Veteriner Fakultesi Dergisi
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, Directory of Open Access Journals, TR DİZİN (ULAKBİM), Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO)
  • Sayfa Sayıları: ss.395-399
  • Anahtar Kelimeler: Egg-laying prediction, Individual productivity, Machine learning, Precision livestock farming, Quail
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Accurate monitoring of individual productivity in quail farming is essential for maximizing economic returns and shaping effective, sustainable breeding programs. However, daily variation in egg-laying behavior across individuals presents significant challenges to consistent productivity prediction. While conventional methods often struggle to account for these irregular and long-term behavioral trends, machine learning techniques offer a promising alternative through their capacity for individualized, data-driven modeling. In this study, we propose a machine learning-based approach to forecast daily egg-laying outcomes by identifying production patterns from a longitudinal dataset comprising 193 days of continuous observations on 371 quails. The predictive feature set includes rolling averages, cumulative production metrics, and prior-day laying status engineered from each bird's historical production data. Three supervised classification models, Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) were employed to predict egg-laying on a daily basis. Performance was assessed using accuracy, precision, recall, and F1-score. Among the models, XGBoost outperformed the others, achieving an F1-score of 91.2% and a recall of 97% on the test set. Feature importance analysis identified the 7-day rolling mean as the most influential predictor. These findings underscore the value of machine learning approaches in modeling individual-level laying patterns and demonstrate their potential application in selection decisions, flock-level management, and automated performance monitoring systems in poultry production.