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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

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:

algorithm computer vision key point detection swine

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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.