Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms

Hasan Önder; Cem Tirink; Taras Yakubets; Andriy Getya; Mykhalio Matvieiev; Ruslan Kononenko; Uğur Şen; Çağri Özgür Özkan; Tolga Tolun; Fahrettin Kaya

doi:10.1002/vms3.70149

Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms

Vet Med Sci. 2025 Jan;11(1):e70149. doi: 10.1002/vms3.70149.

Authors

Affiliations

¹ Faculty of Agriculture, Department of Animal Science, Ondokuz Mayis University, Samsun, Türkiye.
² Faculty of Agriculture, Department of Animal Science, Igdir University, Iğdır, Türkiye.
³ Faculty of Livestock Raising and Water Bioresources, Department of Genetics, Breeding and Reproductive Biotechnology, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine.
⁴ Faculty of Livestock Raising and Water Bioresources, Department of Dairy and Beef Production Technology, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine.
⁵ Faculty of Livestock Raising and Water Bioresources, Department of Hydrobiology and Ichthyology, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine.
⁶ Faculty of Agriculture, Department of Agricultural Biotechnology, Ondokuz Mayis University, Samsun, Türkiye.
⁷ Faculty of Agriculture, Department of Animal Science, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.
⁸ Faculty of Agriculture, Department of Bioengineering, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.
⁹ Andırın Vocational School, Department of Computer Technologies, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.

Abstract

Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals. A highly efficient gradient-boosting decision tree (LightGBM), eXtreme gradient-boosting (XGBoost) and support vector machine (SVM) algorithms were evaluated and compared to the prediction of BW. The coefficient of determination, root mean square error and mean absolute error values were used as comparison criteria. The results showed that LightGBM, XGBoost and SVM algorithms were well fit for the BW using the biometric measures with over 95% accuracy for both train and test sets. The BL was determined as the most explanatory variable on body weight.

Keywords: LightGBM; XGBoost; prediction; rabbit; support vector machine.

MeSH terms

Algorithms
Animals
Body Weight*
Female
Rabbits
Support Vector Machine*