Early prediction of final body weight in Hanwoo steers using machine and deep learning models

Accurate early prediction of final body weight (BW) is essential for optimizing feeding strategies and slaughter planning in beef cattle production. This study compared the performance of three machine learning models (k-nearest neighbors, random forest, and eXtreme Gradient Boosting) and one deep learning model (long short-term memory [LSTM]) to forecast the final BW of Hanwoo steers at various time points prior to slaughter.

A total of 196 Hanwoo steers (7 to 31 months of age) from a commercial farm were utilized. Input data included monthly BW and feed nutrient intake (crude protein, ether extract, neutral detergent fiber, and total digestible nutrients) across three growth stages. Six input configurations (I1-I6) were designed to predict the final BW at 17, 13, 9, 6, 3, and 1 month(s) before slaughter, with a target age of 31 months. The machine and deep learning models were assessed by five-fold cross-validation (training set) and a test set and evaluated via the coefficient of determination (R²) and root mean squared error (RMSE).

Among the tested models, the LSTM achieved the highest prediction accuracy across all the configurations. The performance of the LSTM improved as the prediction point approached the target slaughter age: I1 (R² = 0.60, RMSE = 52.80), I2 (0.72, 45.40), I3 (0.76, 40.92), I4 (0.83, 35.84), I5 (0.90, 33.12), and I6 (0.97, 22.62).

These results demonstrated that LSTM effectively captured temporal dependencies in sequential data, enabling more accurate BW forecasting under commercial conditions. While I6 achieved the highest prediction accuracy, the 3-6 month predictions (I4 and I5) demonstrated reasonably high accuracy, which could provide a practical timeframe for farm-level management and planning. This approach could be used in evidencebased decision-making in Hanwoo production by providing reliable predictions well before slaughter.