ArticleVeterinary medicine and science2025
Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms.
Article in Veterinary medicine and science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluation of the performance of the Light Gradient Boosting Machine (LightGBM) algorithm in predicting live weight in different goat breeds.BMC veterinary research · 2026Article
- Prediction model for daily feed intake during the growing period in floor-reared Wanxi White Geese based on machine learning and feeding behavior features.Poultry science · 2026Article
- Application of Machine Learning Algorithms in Estimating Live Weight of Yucatecan Criollo Pigs Through Biometric Measurements.Animals : an open access journal from MDPI · 2026Article
- Enhancing Rabbit Welfare in Intensive Production Systems: A Review.TheScientificWorldJournal · 2026Review
- Recent feeding history drives prediction of daily feed intake in lactating sows.Translational animal science · 2026Article
- Smarter ways to predict rabbit body weight across multiple breeds.Journal of advanced veterinary and animal research · 2025Article
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Authors and funding
10 authors.
Funding
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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.
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