Universiti Putra Malaysia

This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.

This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.