[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125831-en":3,"doc-seo-125831-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125831,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning algorithms for the prediction of EUROP classification grade and carcass weight - using 3-dimensional measurements of beef carcasses","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.","Scotland's Rural College  \nMachine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses  \nNisbet, H; Lambe, NR; Miller, G A; Doeschl-Wilson, Andrea; Barclay, David; Wheaton, Alex; Duthie, C-A  \nPublished in:  \nFrontiers in Animal Science  \nDOI:  \n10.3389/fanim.2024.1383371  \nPrint publication: 25/06/2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication  \nCitation for pulished version (APA):  \nNisbet, 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)  \nGeneral rights  \nCopyright 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.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf 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.  \nDownload date: 12. Sep. 2025  \nTYPE Original Research PUBLISHED 25 June 2024  \nDOI 10.3389/fanim.2024.1383371  \nOPEN ACCESS  \nEDITED BY  \nVirginia C. Resconi, University of Zaragoza, Spain  \nREVIEWED BY  \nSeveriano Silva,  \nUniversidade de Tra´s-os-Montes e Alto, Portugal  \nJuliana Petrini,  \nUniversity of São Paulo, Brazil  \n*CORRESPONDENCE Holly Nisbet  \n [holly.nisbet@sruc.ac.uk](holly.nisbet@sruc.ac.uk)  \nRECEIVED 07 February 2024  \nACCEPTED 09 May 2024  \nPUBLISHED 25 June 2024  \nCITATION  \nNisbet 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.  \nFront. Anim. Sci. 5:1383371 .  \ndoi: 10.3389/fanim.2024.1383371  \nCOPYRIGHT  \n© 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.  \nMachine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses  \nHolly Nisbet 1,2*, Nicola Lambe 1, Gemma A. Miller 1,  \nAndrea Doeschl-Wilson 2, David Barclay 3, Alexander Wheaton 3 and Carol-Anne Duthie 1  \n1Agriculture 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  \nIntroduction: 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","cbCaigt4GIZ4DtmQ","https://ap.wps.com/l/cbCaigt4GIZ4DtmQ","pdf",1218805,1,19,"English","en",105,"# Introduction\n# Methods\n## Data and features\n## Machine learning models\n# Results\n# Discussion","[{\"question\":\"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？\",\"answer\":\"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。\"},{\"question\":\"本研究采用了哪些机器学习方法？\",\"answer\":\"研究使用了随机森林（random forests）和人工神经网络（artificial neural networks）。\"},{\"question\":\"加入3D尺寸测量后，预测性能如何？\",\"answer\":\"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。\"}]","Machine learning algorithms for the prediction of EUROP classification grade and carcass weight - using 3-dimensional measurements of beef carcasses | PDF",1785901464,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-algorithms-for-the-prediction-of-europ-classification-grade-and-carcass-weight-using-3-dimensional-measurements-of-beef-carcasses","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-for-the-prediction-of-europ-classification-grade-and-carcass-weight-using-3-dimensional-measurements-of-beef-carcasses/125831/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"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？","Question",{"text":75,"@type":76},"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。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本研究采用了哪些机器学习方法？",{"text":80,"@type":76},"研究使用了随机森林（random forests）和人工神经网络（artificial neural networks）。",{"name":82,"@type":73,"acceptedAnswer":83},"加入3D尺寸测量后，预测性能如何？",{"text":84,"@type":76},"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。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]