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Prediction of sheep carcass traits from early-life records using machine learning

Shahinfar, S., Kelman, K.R.ORCID: 0000-0002-4877-3112 and Kahn, L. (2019) Prediction of sheep carcass traits from early-life records using machine learning. Computers and Electronics in Agriculture, 156 . pp. 159-177.

Link to Published Version: https://doi.org/10.1016/j.compag.2018.11.021
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Abstract

Currently hot carcass weight (HCW) and fat score jointly indicate the price grid for sheep meat in Australia. However, experts in the field believe that soon, yield and quality traits such as intramuscular fat (IMF), greville rule fat depth (GRFAT), computed tomography lean meat yield (CTLEAN), and loin weight (LW) are likely to play a role in pricing. Having an accurate prediction of these traits earlier in the life of an animal will allow sheep producers to adjust their management practices in order to achieve the target market requirements. Management, genetics, pasture and climate factors, influence these traits directly and epistatically. Traditional prediction methods may not be powerful enough to capture complex interactions while avoiding overfitting. In this case, learning algorithms that can learn from the current data to predict the animal's future performance offers promise. In this study, five different types of Machine Learning (ML) algorithm, namely Deep Learning (DL), Gradient Boosting Tree (GBT), K-Nearest Neighbour (KNN), Model Tree (MT), and Random Forest (RF) were employed to predict HCW, IMF, GRFAT, LW and CTLEAN and their performances were compared against linear regression (LR) as the gold standard of multinomial prediction. Four scenarios representing different numbers of weight recordings-from a total of 9 weight measures taken between birth (WT1) and pre-slaughter (WT9)- were used to inform the algorithms and all models were trained and tested under equal conditions with identical training and testing sets. Selection of the most effective subset of predictor features were completed via greedy stepwise search among all the available features jointly with expert opinion. In predicting all the traits, RF was superior while LR and KNN showed the lowest prediction performance. When using the final model for predicting on an independent test set, the scenario with the most accurate prediction performance differed across traits. IMF and GRFAT were most accurately predicted when using birth, weaning, and pre-slaughter weights, while the most accurate scenario for HCW, LW and CTLEAN utilised weaning, six monthly weight measures after weaning and pre-slaughter weight. Across all scenarios the least accurate prediction was for IMF.

Item Type: Journal Article
Murdoch Affiliation: School of Veterinary and Life Sciences
Publisher: Elsevier B.V.
Copyright: © 2018 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/43551
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