Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows

Ruchay, Alexey; Kober, Vitaly; Dorofeev, Konstantin; Колпаков, В. И.; Джуламанов, К. М.; Kalschikov, Vsevolod; Guo, Hao

doi:10.1016/j.compag.2022.106837

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…On the other hand, rump height has a high direct effect value in predicting BW in Nguni cattle [ 19 ]. In Hereford cows, metacarpus girth and backside half-girth are important variables in estimating live weight [ 20 ]. Therefore, CG and BL can be selection criteria for crossbreeding between Belgian Blue and FH.…”

Section: Resultsmentioning

confidence: 99%

Body weight prediction of Belgian Blue crossbred using random forest

Praharani,

Talib,

Kusumaningrum

et al. 2024

J Adv Vet Anim Res

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Objective: The aim of this study was to predict the body weight (BW) of a Belgian Blue X Friesian Holstein (BB X FH) crossbred in Indonesia based on morphometrics using random forest. Materials and Methods: A total of 26 BB X FH crossbreds were observed for BW, chest weight (CW), body length (BL), hip height (HH), wither height (WH), and chest girth (CG) from 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 days of age. Stepwise regression and random forest were performed using R 3.6.1. Results: The random forest results show that CG is an important variable in estimating BW, with an important variable value of 24.49%. Likewise, the results obtained by stepwise regression show that CG can be an indicator of selection for the BB X FH crossbred. The R squared value obtained from the regression is 0.83, while the R squared value obtained from the random forest (0.86) is greater than the regression. Conclusion: In conclusion, random forest produces a better model than stepwise regression. However, a good simple equation to use to estimate BW is CG.

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Section: Resultsmentioning

confidence: 99%

Body weight prediction of Belgian Blue crossbred using random forest

Praharani,

Talib,

Kusumaningrum

et al. 2024

J Adv Vet Anim Res

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“…The breed is considered an appropriate categorization indicator in predicting cattle body weight due to the distinct phenotypic and conformational characteristics of each cattle breed [ 25 ]. Future studies should consider the use of machine learning-based algorithms, as reported by Ruchay et al [ 26 ]. This is because the extra tree regressor algorithm, using morphometric measurements and cattle age, provided better results than regression for predicting body weight.…”

Section: Discussionmentioning

confidence: 99%

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

Firdaus,

Atmoko,

Ibrahim

et al. 2024

J Adv Vet Anim Res

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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.

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“…Measurements of exterior features and meat productivity were made using a system of accurate measurement of the body of live cattle using three-depth chambers and non-rigid three-dimensional shape restoration [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Ruchay

Колпаков

Kosyan

et al. 2022

Animals

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In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip (Illumina Inc., San Diego, CA, USA), containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.

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Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows

Cited by 16 publications

References 21 publications

Body weight prediction of Belgian Blue crossbred using random forest

Body weight prediction of Belgian Blue crossbred using random forest

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

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

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