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High-Accuracy Chicken Breed Identification Using Microsatellite Genotype Data and AutoGluon Framework.

Rajaonarison Faniriharisoa Maxime Toky1, Sutthisak Sukhamsri2, Sadeep Medhasi1

  • 1Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.

Biology
|January 10, 2026
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Summary

Machine learning, specifically the random forest model, accurately identifies chicken breeds using microsatellite data. This cost-effective method enhances breed conservation and breeding program design.

Keywords:
breed determinationchicken breedsmachine learningmicrosatelliterandom forest

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Area of Science:

  • Bioinformatics
  • Animal Genetics
  • Machine Learning Applications

Background:

  • Accurate chicken breed identification is crucial for conservation and breeding programs.
  • Distinguishing phenotypically similar chicken breeds is challenging and expensive.
  • Machine learning (ML) offers advanced tools for analyzing complex genetic data.

Purpose of the Study:

  • To develop an accessible and optimized methodology for chicken breed identification.
  • To evaluate the effectiveness of the random forest (RF) model for classifying chicken breeds using microsatellite data.
  • To demonstrate the utility of ML, particularly AutoGluon, for cost-effective breed determination.

Main Methods:

  • Utilized microsatellite genotype data from 651 individuals across 30 chicken populations.
  • Employed a random forest (RF) model for breed classification.
  • Applied cross-validation techniques (10-fold and leave-one-out) and performance metrics (accuracy, Cohen's Kappa, F1-score).

Main Results:

  • The RF model achieved 95.38% accuracy on the testing dataset.
  • Cross-validation accuracies were 91.44% (10-fold) and 90.99% (leave-one-out).
  • The trained model demonstrates generalizability for chicken breed determination.

Conclusions:

  • Machine learning, particularly RF and automated approaches like AutoGluon, provides a robust framework for chicken breed identification.
  • This ML approach offers a straightforward, modern, and cost-effective method for breed determination.
  • Larger datasets are expected to further improve model performance for diverse chicken breeds.