Predicting carcass cut yields in cattle from digital images using artificial intelligence

Meat Sci. 2022 Feb:184:108671. doi: 10.1016/j.meatsci.2021.108671. Epub 2021 Sep 10.


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.

Keywords: Carcass grading; Cattle; Deep learning; Image segmentation; Machine learning; Meat yield.

MeSH terms

  • Animals
  • Body Composition
  • Cattle
  • Deep Learning*
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning
  • Red Meat / classification*