Journal of Animal Breeding and Genomics (J Anim Breed Genom)
Dong Hui Lee1, Young Gyu Cho1, Kefala1, Chang Wan Sun2, Dong Jun Kim2, Myoung Jae Lee3, Kwang Jei Woo4, Soohyung Kim4, Kang Seok Seo1*
1Department of Animal Science & Technology, Sunchon National University
2Korea Institute For Animal Products Qulaity Evaluation
3BrandContents Inc.
4AIET Inc.
Correspondence to Kang Seok Seo, E-mail: sks@scnu.ac.kr
Volume 6, Number 1, Pages 19-26, March 2022.
Journal of Animal Breeding and Genomics 2022, 6(1), 19-26. https://doi.org/10.12972/jabng.20220003
Received on January 03, 2022, Revised on March 30, 2022, Accepted on March 30, 2021, Published on March 31, 2022.
Copyright © 2022 Korean Society of Animal Breeding and Genetics.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0).
Data were collected from 2,730 heads of Korean cattle between August 2021 and September 2021 and body condition score (BCS) and body composition traits were estimated based on image data for the prediction of meat quantity traits. The results showed that the estimated BCS and body composition traits had a significant effect on meat quantity traits estimation. Backfat thickness and yield index showed low R2 in the multivariate model and also a correlation (Pearson`s) results appeared to be contrary with the results of other studies. Hence, it is considered necessary to check the raw data. However, it was confirmed that BCS and body composition traits can be used as indicators for predicting meat quantity traits in this study. It is thought that an accurate model will be made if the supplemented data will be used in future research.
BCS, Body composition, Meat quantity, Image, Significant
This research (paper) used datasets from ‘The Open AI Dataset Project (AI-Hub, S.Korea)’. All data information can be accessed through ‘AIHub (www.aihub.or.kr)’.
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