Accurate livestock body weight prediction is a key component of precision livestock farming, as it supports herd monitoring, production management, and planning in response to the increasing global demand for meat. Existing approaches for weight prediction include age-based regression models, growth trajectory modelling, average daily gain estimation, and methods relying on morphometric measurements or image-derived features. However, many of these approaches require frequent measurements or specialized data acquisition systems, which are often costly and difficult to deploy under practical farming conditions. This study presents a comparative evaluation of data-driven models for livestock body weight trajectory prediction under low-measurement conditions. A matrix factorization approach and four ensemble-based machine learning methods, namely XGBoost, LightGBM, CatBoost, and ExtraTrees, were evaluated using a dataset of Holstein cows. Model performance was assessed using standard regression metrics, including root mean squared error, mean absolute error, and mean absolute percentage error, with five-fold cross-validation employed to ensure robustness. The results show that ensemble learning methods consistently outperform matrix factorization techniques when only a limited number of weight measurements per animal are available. More specifically, XGBoost achieves the best predictive performance when only one historical measurement per animal is available, whereas ExtraTrees provides the most accurate predictions when two or three historical measurements are available. These findings demonstrate that accurate and cost-effective livestock weight prediction can be achieved from sparse routine body weight records, without relying on dense longitudinal sampling, image-based systems, or extensive morphometric measurements, thereby supporting the practical deployment of predictive tools in precision livestock farming systems.
Hakem et al. (Thu,) studied this question.