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

Frediansyah Firdaus1, Bayu Andri Atmoko1, Alek Ibrahim1

  • 1Research Center for Animal Husbandry, National Research and Innovation Agency, Cibinong Science Center, Bogor, Indonesia.

Journal of Advanced Veterinary and Animal Research
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

Predicting beef cattle body weight using digital images is possible. The top view offers the highest accuracy, while body length or chest depth are practical alternatives considering breed and sex.

Keywords:
Body weightbeef cattledigital imagemeta-analysisprediction

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Area of Science:

  • Agricultural Science
  • Animal Science
  • Computer Vision

Background:

  • Accurate body weight prediction is crucial for beef cattle management and economic evaluation.
  • Traditional methods of weighing cattle can be labor-intensive and stressful for animals.
  • Digital image processing offers a non-invasive alternative for estimating body weight.

Purpose of the Study:

  • To develop and validate a meta-analysis method for predicting beef cattle body weight using digital image processing.
  • To identify the most effective digital image variables for body weight prediction.
  • To assess the influence of cattle breed and sex on prediction accuracy.

Main Methods:

  • A meta-analysis was conducted on 13 selected studies involving 3,017 beef cattle.
  • Keywords used for literature search included "beef cattle," "correlation," "digital image," and "body weight."
  • Digital image measurements included wither height, hip height, chest depth, body length, and top view. Correlation coefficients were used as effect sizes.

Main Results:

  • The top view variable showed the highest correlation with body weight.
  • Wither height exhibited significant differences in correlation coefficients across breeds (Hanwoo: 0.94, Holstein: 0.79, Simmental: 0.66).
  • Sex influenced correlation coefficients for wither height (males: 0.73, females: 0.90) and hip height (males: 0.70, females: 0.87).

Conclusions:

  • Digital image analysis, particularly the top view, is effective for predicting beef cattle body weight.
  • Body length and chest depth are viable alternatives for practical field applications.
  • Incorporating breed and sex into predictive models enhances accuracy.