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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Predictive modeling of bruising in broiler chickens using machine learning algorithms.

Pranee Pirompud1, Panneepa Sivapirunthep2, Veerasak Punyapornwithaya3

  • 1Doctoral Program in Innovative Tropical Agriculture, Department of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

Poultry Science
|September 3, 2025
PubMed
Summary

Machine learning models can predict broiler chicken bruising. The Extreme Gradient Boosting (XGB) algorithm showed the best performance in identifying flocks at high risk of bruising, aiding welfare and economic outcomes.

Keywords:
Extreme gradient boostingLairage timeModel performanceVariable importance

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Area of Science:

  • Animal Science
  • Agricultural Engineering
  • Data Science

Background:

  • Bruising in broiler chickens is a significant issue in poultry production, negatively impacting animal welfare and economic returns.
  • Physical stress and injury during rearing, catching, transport, and slaughter contribute to carcass bruising.

Purpose of the Study:

  • To classify broiler bruising risk (low or high percentage of carcass bruising) per truckload.
  • To compare the predictive performance of six machine learning models for early bruising detection.
  • To provide tools for decision-making to enhance welfare monitoring and reduce economic losses.

Main Methods:

  • Evaluated six machine learning models: LASSO, CT, RF, NB, SVM, and XGB.
  • Utilized a dataset of 26,031 truckloads with 14 predictors from rearing to slaughter phases.
  • Trained models without resampling due to the natural distribution of high bruising percentages (41.4% of truckloads).

Main Results:

  • The Extreme Gradient Boosting (XGB) algorithm demonstrated superior predictive performance compared to the other five models.
  • Key predictors for bruising included mean body weight, transport duration, stocking density (housing and crate), transport distance, mortality/culling rates, feed withdrawal time, and lairage duration.
  • Overall predictive accuracy was moderate, potentially due to missing critical variables.

Conclusions:

  • Machine learning, particularly XGB, is a valuable tool for the early identification of broiler flocks susceptible to bruising.
  • Targeted management interventions focusing on bird health, stocking density, feed withdrawal, and lairage duration can effectively mitigate bruising.
  • Implementing these strategies can significantly improve both animal welfare and operational efficiency in commercial broiler production.