Date of Award
Masters of Science (Research)
Beef carcass classification in Europe is predicated on the EUROP grid for both fatness and conformation. Although this system performs well for grouping visually similar carcasses, it cannot be used to accurately predict meat yields from these groups, especially when considered on an individual cut level. Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be fully proven in a regression scenario using carcass images. Here we have trained DL models to predict carcass cut yields and compared predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of two categories of cuts, namely 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 machine learning (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. For Grilling cuts, models developed in Approach 1 had the lowest coefficient of determination (R2) compared to the two other approaches. Deep Learning models had a slightly improved performance for Grilling cuts but approach 3 performed best. Similarly, for Roasting cuts approach 3 performed best, whereas approaches 1 and 2 performed similarly. Our results show that DL models can be trained to predict carcass cuts but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms. However, as our DL models use only image data these models can be deployed more practically at an abattoir level.
Matthews, Darragh, "Predicting carcass cut yields in cattle from digitalimages using artificial intelligence" (2021). Theses [online].
Available at: https://sword.cit.ie/allthe/9
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