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Automating hock wound detection in dairy cattle.

W Flanders¹, P S Basran¹, M Wieland²

¹Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

JDS Communications

|January 29, 2025

Summary

Manual hock scoring in dairy cattle shows inconsistency. Artificial intelligence offers a promising automated solution for more reliable and objective assessment of cow hock health, improving animal welfare.

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Area of Science:

Animal Welfare Science
Veterinary Medicine
Artificial Intelligence in Agriculture

Background:

Hock scoring is a critical welfare indicator in dairy cattle, assessing hock condition for injuries and lesions.
Inconsistent manual scoring by observers can compromise the reliability of this vital welfare assessment.
Existing methods highlight a need for more objective and consistent approaches to evaluate dairy cow hock health.

Purpose of the Study:

To quantify the inconsistency of manual hock scoring in dairy cattle.
To explore the potential of artificial intelligence for automating hock score detection.
To develop a more reliable and objective method for assessing dairy cattle hock health.

Main Methods:

Two studies were conducted to measure manual scoring reproducibility and repeatability.
Repeatability was assessed using a weighted Cohen's kappa metric.
A U-net semantic segmentation algorithm was employed for automated wound detection on cow hocks.

Main Results:

Manual hock scoring demonstrated significant inconsistency.
Manual scoring was found to be more consistent than video scoring.
The U-net algorithm successfully detected hock wounds, indicating potential for automation.

Conclusions:

Variability in manual hock scoring necessitates more objective assessment methods.
Artificial intelligence-driven automation can reduce observer bias and improve scoring consistency.
Automated hock scoring promises enhanced dairy cattle welfare through efficient and accurate health monitoring.