Leveraging Visual Data for Accurate Body Weight Prediction in Surti Goats
TL;DR
The findings show that combining visual and morphometric information under a strict group-based evaluation provides reliable, non-contact body weight estimation on unseen livestock.
Abstract
Routine weighing of goats requires suitable equipment and often involves animal handling or restraint, making repeated monitoring difficult under farm conditions. This study estimated Surti goat body weight by combining image-derived morphometric measurements (heart girth and body length extracted from two-dimensional images) with age and sex metadata. Mask Region-based Convolutional Neural Network (Mask R-CNN) was used to separate each goat from the image background before the relevant visual measurements were extracted. Dataset partitioning was conducted strictly at the Animal ID level (group-wise split) to prevent data leakage and evaluate true model generalisation on unseen animals. XGBoost, Random Forest and CatBoost were trained using the combined feature set, and their outputs were integrated through a weighted ensemble with weights of 0.5, 0.3, and 0.2 assigned to XGBoost, CatBoost, and Random Forest, respectively. On the validation set (37 goats), XGBoost achieved an 𝑅2 of 0.8514 (RMSE = 4.26 kg, MAE = 3.47 kg, MAPE = 16.27%). On the completely unseen test set (37 goats), XGBoost achieved the highest predictive performance with an 𝑅2 of 0.7955 (MSE = 29.15 kg², RMSE = 5.40 kg, MAE = 4.01 kg, and MAPE = 16.38%), while the weighted ensemble recorded an 𝑅2 of 0.7713 (MSE = 32.60 kg², RMSE = 5.71 kg, MAE = 4.13 kg, and MAPE = 16.60%). The findings show that combining visual and morphometric information under a strict group-based evaluation provides reliable, non-contact body weight estimation on unseen livestock.