Machine learning approaches for early prediction of adult wool growth and quality in Australian Merino sheep

Artificial Neural Networks (NN), Model Tree (MT) and Bagging (BG) were used to carry out these predictions and their performance was compared with Linear Regression (LR) as the gold standard of prediction. The NN method had the poorest performance in all five traits. MT and BG had very similar performance and for a number of practical reasons, our method of choice was MT for early prediction of adult wool traits. The correlation coefficients of MT predictions were 0.93, 0.90, 0.94, 0.81 and 0.59 with Mean Absolute Error of 0.48 kg, 0.41 kg, 0.92 µm, 6.91 mm and 6.82 N/ktex, for predicting aGFW, aCFW, aFD, aSL, and aSS respectively.