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Beef Science | Meat Science
Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of Grilling cuts and Roasting cuts from a large dataset of 54,598 and 69,246 animals respectively. The approaches taken were (1) animal phenotypic data used as features for a range of ML algorithms, (2) carcass images used to train Convolutional Neural Networks, and (3) carcass dimensions measured directly from the carcass images, combined with the associated phenotypic data and used as feature data for ML algorithms. Results showed that DL models can be trained to predict carcass cuts yields but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms.
Daragh Matthews, Thierry Pabiou, Ross D. Evans, Christian Beder, Aengus Daly, Predicting carcass cut yields in cattle from digital images using artificial intelligence,Meat Science, Volume 184,2022, Available online at: https://doi.org/10.1016/j.meatsci.2021.108671.
Available for download on Thursday, October 13, 2022