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

Aug 5, 2026

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Mechanical grading can classify beef carcasses objectively, yet adoption is limited by infrastructure and equipment costs. This study evaluates machine learning methods, including random forests and artificial neural networks, to predict EUROP conformation grade, fat class, and cold carcass weight from 3-dimensional imaging features (widths, lengths, volumes) alongside fixed effects such as kill date, breed type, and sex. Adding 3D measurements improves predictive accuracy across traits, with random forests showing R2=0.72 for weight and strong conformation performance, while neural networks achieve comparable weight and conformation results and moderate fat-class accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Methods

Data and features

Machine learning models

Results

Discussion

Scotland's Rural College Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses Nisbet, H; Lambe, NR; Miller, G A; Doeschl-Wilson, Andrea; Barclay, David; Wheaton, Alex; Duthie, C-A Published in: Frontiers in Animal Science DOI: 10.3389/fanim.2024.1383371 Print publication: 25/06/2024 Document Version Publisher's PDF, also known as Version of record Link to publication Citation for pulished version (APA): Nisbet, H. , Lambe, NR. , Miller, G. A. , Doeschl-Wilson, A. , Barclay, D. , Wheaton, A. , & Duthie, C.-A. (2024) . Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses. Frontiers in Animal Science , 5, Article 1383371. [https://doi.org/10.3389/fanim.2024.1383371](https://doi.org/10.3389/fanim.2024.1383371) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 12. Sep. 2025 TYPE Original Research PUBLISHED 25 June 2024 DOI 10.3389/fanim.2024.1383371 OPEN ACCESS EDITED BY Virginia C. Resconi, University of Zaragoza, Spain REVIEWED BY Severiano Silva, Universidade de Tra´s-os-Montes e Alto, Portugal Juliana Petrini, University of São Paulo, Brazil *CORRESPONDENCE Holly Nisbet [holly.nisbet@sruc.ac.uk](holly.nisbet@sruc.ac.uk) RECEIVED 07 February 2024 ACCEPTED 09 May 2024 PUBLISHED 25 June 2024 CITATION Nisbet H, Lambe N, Miller GA, Doeschl-Wilson A, Barclay D, Wheaton A and Duthie C-A (2024) Machine learning algorithms for the prediction of EUROP classiﬁcation grade and carcass weight, using 3-dimensional measurements of beef carcasses. Front. Anim. Sci. 5:1383371 . doi: 10.3389/fanim.2024.1383371 COPYRIGHT © 2024 Nisbet, Lambe, Miller, Doeschl-Wilson, Barclay, Wheaton and Duthie. This is an openaccess 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) and the copyright owner(s) 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. Machine learning algorithms for the prediction of EUROP classiﬁcation grade and carcass weight, using 3-dimensional measurements of beef carcasses Holly Nisbet 1,2*, Nicola Lambe 1, Gemma A. Miller 1, Andrea Doeschl-Wilson 2, David Barclay 3, Alexander Wheaton 3 and Carol-Anne Duthie 1 1Agriculture and Land Based Engineering, Scotland’s Rural College, Edinburgh, United Kingdom, 2The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom, 3 Engineering Department, Northern Agri-Tech Innovation Hub, Innovent Technology Ltd, Midlothian, United Kingdom Introduction: 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 adv

FAQs

为什么研究要使用3D成像与机器学习来预测EUROP分级和胴体重？

传统机械分级客观但在行业中应用不足，主要受基础设施和设备成本限制。3D影像系统可提供可用于客观分级的尺寸测量，而机器学习可将这些数据用于准确预测关键性状。

本研究采用了哪些机器学习方法？

研究使用了随机森林（random forests）和人工神经网络（artificial neural networks）。

加入3D尺寸测量后，预测性能如何？

与不包含3D测量的模型相比，加入3D尺寸后在多个性状上提升了预测准确性。随机森林对冷胴体重达到R2=0.72，并在分级准确性方面表现较好；神经网络在冷胴体重与体型等级上也表现出相近的较高准确度。

Aug 5, 2026

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