Application of machine learning algorithms to predict body condition score from liveweight records of mature romney ewes

Abstract

3.5) scale was used due to high‐class imbalance in the five‐scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (> 85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.