Automated Screening for Pig Pathologies at Abattoir

Motivation and Objectives

Visual inspection of carcasses is an important factor for ensuring the quality of meat products. However, manual inspection puts a strain on meat inspector resources, which effectively prevents detailed screening for the purposes of health schemes.

The specific aims of this project are:

  • To develop an automated system based on visual image analysis to screen pig carcasses and offal at abattoir, enabling the detection of public health hazards and signs of subclinical diseases.
  • To feed data associated with subclinical diseases to pig producers, who will then be able to make decisions about improving the health of their herds.

The project will enhance confidence in detecting health hazards in pig carcasses and offal, aiming towards automated detection of underlying subclinical disease. Feeding this back to pig farmers will increase productivity and improve efficiency on farms through preventing further diseases. Producers will be able to make decisions about improving the health of their herd through the information they receive from the abattoir. The project will thus contribute towards sustainability and competitiveness of the UK pig industry.


March 2014 - May 2017 (39 months)


Innovate UK (former Technology Strategy Board)
Biotechnology and Biological Sciences Research Council (BBSRC)


The project brings together market leaders in meat production Tulip Ltd, suppliers of systems for abattoirs Hellenic Systems Ltd, pig levy board AHDB Pork, the UK's leading centre for research into pig science at the School of Agriculture, Food & Rural Development of the Newcastle University, and experts in computer vision and pattern recognition from the School of Computing Science of the Newcastle University and from the School of Computing of the University of Dundee.


Tulip Ltd

Fiona Glaves (project leader)
Jen Waters

Hellenic Systems Ltd

Terry Carter

Newcastle University

Thomas Plötz
Ilias Kyriazakis
Telmo Amaral

University of Dundee

Stephen McKenna

Built on Skeleton framework.