A first meta-analysis study on body weight prediction method for beef cattle based on digital image processing.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Accounts of Chemical Research Pub Date : 2024-03-31 eCollection Date: 2024-03-01 DOI:10.5455/javar.2024.k760
Frediansyah Firdaus, Bayu Andri Atmoko, Alek Ibrahim, Tristianto Nugroho, Endang Baliarti, Panjono Panjono
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引用次数: 0

Abstract

Objective: This study aimed to develop a method for predicting the body weight of beef cattle using meta-analysis based on digital image processing.

Materials and methods: The meta-analysis process commenced by collecting studies with the keywords "beef cattle," "correlation," "digital image," and "body weight" from Google Scholar and Science Direct. The obtained studies were reviewed papers based on their titles, abstracts, and content, and then categorized by authors, year, country, sample size, and correlation coefficient. A digital image of body measurements used included wither and hip height, chest depth, heart girth, body length, and top view. The statistical analysis was conducted by calculating effect sizes using the correlation coefficient and sample sizes.

Results: The results of the meta-analysis, based on 3,017 cattle from 13 selected studies, showed the highest and lowest correlation coefficients for the top view variable and hip height. Based on cattle breed, significant differences (p < 0.05) were observed in the wither height variable with correlation coefficients of 0.94, 0.79, and 0.66 for Hanwoo, Holstein, and Simmental, respectively. Based on sex, significant differences (p < 0.05) were seen in the wither height variable, with correlation coefficients of 0.73 for males and 0.90 for females, while for hip height, the values were 0.70 and 0.87, respectively.

Conclusion: In conclusion, to achieve the best accuracy in predicting the body weight of beef cattle based on a digital image, the top view variable can be used. However, for ease of field experimentation, body length or chest depth can also be used while taking breed and sex categories into the model.

基于数字图像处理的肉牛体重预测方法的首次荟萃分析研究。
研究目的本研究旨在开发一种基于数字图像处理的荟萃分析预测肉牛体重的方法:在元分析过程中,首先从谷歌学术(Google Scholar)和科学直通车(Science Direct)中收集以 "肉牛"、"相关性"、"数字图像 "和 "体重 "为关键词的研究。根据标题、摘要和内容对获得的研究论文进行审查,然后按照作者、年份、国家、样本量和相关系数进行分类。使用的身体测量数字图像包括枯干和臀高、胸深、心宽、体长和俯视图。统计分析采用相关系数和样本量计算效应大小:基于 13 项选定研究的 3 017 头牛的元分析结果显示,顶视图变量和臀高的相关系数最高和最低。根据牛的品种,汉和牛、荷斯坦牛和西门塔尔牛在髋高变量上存在显著差异(p < 0.05),相关系数分别为 0.94、0.79 和 0.66。从性别来看,雄性和雌性在凋萎高度变量上存在显著差异(p < 0.05),相关系数分别为 0.73 和 0.90,而髋高的相关系数分别为 0.70 和 0.87:总之,根据数字图像预测肉牛体重的准确性最高,可以使用俯视图变量。不过,为了便于现场实验,在将品种和性别分类纳入模型时,也可使用体长或胸深。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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