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Computer vision system for assessing pig welfare indicators on carcasses.

Francis Ferri¹, Yuanyue Wang², Ryan Ko³

¹Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada.

Veterinary and Animal Science

|July 6, 2026

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A new computer vision system automates pig welfare assessment on carcasses, offering objective, real-time monitoring. This technology enhances food safety and production sustainability by accurately detecting lesions and hernias.

Area of Science:

Animal Welfare Science
Computer Vision
Food Safety

Background:

Societal demand for transparency in farmed animal welfare is rising.
High animal welfare standards correlate with production efficiency, sustainability, and food safety.
Traditional on-farm welfare assessments are subjective, costly, and pose biosecurity risks.

Purpose of the Study:

To develop and validate a real-time computer vision system for automated pig welfare assessment on carcasses.
To provide an objective, scalable, and efficient alternative to traditional welfare monitoring methods.
To complement on-farm assessments by verifying welfare indicators at the slaughter stage.

Main Methods:

A modular pipeline combining YOLOv4 detection and U-Net segmentation for image analysis.

Keywords:

Animal welfare indicators Automated monitoring Hernia Skin lesions Tail lesions

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Evaluation of skin and tail lesions, tail length, and hernias on pig carcasses.

Colorimetric and geometric analysis integrated with custom segmentation and curve-fitting for tail length estimation.

Main Results:

High accuracy achieved for hernias (93.0%), dorsal skin lesions (90.4%), lateral skin lesions (86.3%), and tail lesions (86.8%).
Tail length estimation yielded a root mean squared error (RMSE) of 4.45 cm.
The system operates efficiently at 30.31 frames per second (FPS), enabling real-time industrial application.

Conclusions:

The computer vision system provides a scalable and objective solution for real-time pig welfare monitoring.
Automated carcass assessment can enhance transparency and verify welfare standards in industrial settings.
This technology contributes to improved animal welfare, food quality, and production sustainability.