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

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

Kafkas Universitesi Veteriner Fakultesi Dergisi, vol.32, no.3, pp.395-399, 2026 (SCI-Expanded, Scopus, TRDizin)

  • Publication Type: Article / Article
  • Volume: 32 Issue: 3
  • Publication Date: 2026
  • Doi Number: 10.9775/kvfd.2026.36392
  • Journal Name: Kafkas Universitesi Veteriner Fakultesi Dergisi
  • Journal Indexes: 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)
  • Page Numbers: pp.395-399
  • Keywords: Egg-laying prediction, Individual productivity, Machine learning, Precision livestock farming, Quail
  • Open Archive Collection: AVESIS Open Access Collection
  • Akdeniz University Affiliated: Yes

Abstract

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.