Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

Abstract

Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k-nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0–2.0, 2.5–3.5, > 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.

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System

“…The overall performance for all forecasting models is reported in Table 2. Notably, the machine learning models performed better than the regression models, where the RF and SVM models demonstrated the best overall prediction performance, similar to other livestock-related studies comparing the performance of multiple forecasting models [3,12,13]. Furthermore, the SVM outperformed the other models (Table 3) measured in terms of R 2 (0.731) and lower errors measured by RMSE (0.406) and MAE (0.284).…”

Predicting ewe body condition score using adjusted liveweight for conceptus and fleece weight, height at withers, and previous body condition score record

“…However, a great proportion of variability in BCS remained unaccounted for, leading to less robust models. Furthermore, in our previous study ( Semakula et al, 2021 b), machine learning classification algorithms were successfully (with up to 90% accuracy) used to predict BCS using LW predictors. However, these machine learning classification models were limited to a 3-point scale due to gross class imbalance in BCS data.…”