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Front. Anim. Sci.
Sec. Product Quality
Volume 5 - 2024 | doi: 10.3389/fanim.2024.1383371

Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses Provisionally Accepted

  • 1Scotland's Rural College, United Kingdom
  • 2University of Edinburgh, United Kingdom
  • 3Innovent Technology Ltd, United Kingdom

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Mechanical grading can be used to objectively classify beef carcasses. Despite its many benefits, it is scarcely used within the beef industry, often due to infrastructure and equipment costs. As technology progresses, systems become more physically compact, and data storage and processing methods are becoming more advanced. Purpose-built imaging systems can calculate 3-dimensional measurements of beef carcasses, which can be used for objective grading. This study explored the use of machine learning techniques (random forests and artificial neural networks) and their ability to predict carcass conformation class, fat class and cold carcass weight, using both 3-dimensional measurements (widths, lengths, and volumes) of beef carcasses, extracted using imaging technology, and fixed effects (kill date, breed type and sex). Cold carcass weight was also included as a fixed effect for prediction of conformation and fat classes. Including the dimensional measurements improved prediction accuracies across traits and techniques compared to that of results from models built excluding the 3D measurements. Model validation of random forests resulted in moderate-high accuracies for cold carcass weight (R2=0.72), conformation class (71% correctly classified), and fat class (55% correctly classified). Similar accuracies were seen for the validation of the artificial neural networks, which resulted in high accuracies for cold carcass weight (R2=0.68) and conformation class (71%), and moderate for fat class (57%). This study demonstrates the potential for 3D imaging technology requiring limited infrastructure, along with machine learning techniques, to predict key carcass traits in the beef industry.

Keywords: Beef carcasses1, objective beef classification2, EUROP classification grid3, video image analysis4, beef grading cameras5, machine learning6

Received: 07 Feb 2024; Accepted: 09 May 2024.

Copyright: © 2024 Nisbet, Lambe, Miller, Doeschl-Wilson, Barclay, Wheaton and Duthie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Holly Nisbet, Scotland's Rural College, Edinburgh, United Kingdom