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Machine-learning-based quantitative body condition scoring in Qinchuan beef cattle using conventional body

Shuaicheng Chen1, Guo Li1, Linsen Zan2

  • 1College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China.

Tropical Animal Health and Production
|June 22, 2026
PubMed
Summary

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Objective body condition scoring for beef cattle is now possible using routine measurements and machine learning. This system provides accurate, quantitative assessments for improved cattle management and reproduction.

Area of Science:

  • Animal Science
  • Agricultural Engineering
  • Machine Learning Applications

Background:

  • Conventional body condition scoring (BCS) in beef cattle is subjective and lacks quantitative standards, impacting reproductive and nutritional management.
  • Developing an objective BCS system is crucial for precision livestock farming and efficient herd management.

Purpose of the Study:

  • To develop a low-cost, objective BCS evaluation system for Qinchuan beef cattle.
  • To utilize routine body measurements and machine learning models for quantitative BCS assessment.

Main Methods:

  • Collected data from 321 Qinchuan beef cows (24-36 months old) with BCS scores (4-8) and seven body measurements.
  • Employed ANOVA and Pearson correlation to identify informative traits and tested six composite body condition indices.
Keywords:
Body condition scoreBody measurementsComposite morphometric indexMachine learningPrecision livestock farmingQinchuan beef cattle

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  • Compared seven machine learning algorithms using nested cross-validation and quadratic weighted kappa (QWK) as the primary metric.

    • Main Results:

    • Significant increases in all measured traits across BCS classes were observed.
    • Heart girth, abdominal girth, and body weight showed the strongest correlations with BCS.
    • The k-nearest neighbors model achieved a QWK of 0.89 and 95.5% prediction accuracy within ±1 BCS unit.

    Conclusions:

    • Conventional body measurements, composite indices, and machine learning offer a practical and scalable solution for objective BCS evaluation in Qinchuan beef cattle.
    • This objective system supports precision livestock farming by providing reliable data for management decisions.
    • The developed model enhances the accuracy and consistency of BCS assessment, benefiting cattle reproduction and nutrition.