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Deep learning based landmark detection for measuring hock and knee angles in sows.

Ryan L Jeon1, Joshua M Peschel1, Brett C Ramirez1

  • 1Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, US.

Translational Animal Science
|December 12, 2024
PubMed
Summary

This study introduces a deep learning method for automatically measuring sow hock and knee angles from images, improving lameness detection. This objective approach enhances sow welfare assessments and breeding decisions.

Keywords:
algorithmcomputer visionkey point detectionswine

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Area of Science:

  • Animal Science
  • Computer Vision
  • Biotechnology

Background:

  • Lameness is a major cause of culling in breeding sows, impacting herd productivity.
  • Current visual scoring for lameness detection is subjective, slow, and inconsistent, necessitating objective methods.

Purpose of the Study:

  • To develop and validate a visual deep learning approach for automated hock and knee angle measurement in sows.
  • To provide an objective tool for assessing sow lameness and improving breeding herd management.

Main Methods:

  • A deep learning model was trained to detect key body landmarks from sow images (side and rear profiles).
  • Trigonometric formulae were used to calculate hock and knee angles from the detected landmarks.
  • Automated angle measurements were validated against manual measurements using statistical analysis (RMSE, R², Bland-Altman).

Main Results:

  • The deep learning model achieved high accuracy in landmark detection (mean average precision = 0.94).
  • Automated angle measurements showed strong agreement with manual measurements (average RMSE = 4.13°, average R² = 0.84).
  • Statistical analysis confirmed the reliability and accuracy of the automated system.

Conclusions:

  • The developed visual deep learning approach provides an objective and accurate method for measuring sow hock and knee angles.
  • This technology can be integrated into gilt replacement criteria to optimize sow breeding units and improve animal welfare.
  • The findings are valuable for animal geneticists, scientists, and practitioners in the swine industry.