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Related Experiment Video

Updated: Mar 18, 2026

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A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method.

A Nasirahmadi1, O Hensel2, S A Edwards1

  • 11School of Agriculture, Food and Rural Development,Newcastle University,Newcastle upon Tyne NE1 7RU,UK.

Animal : an International Journal of Animal Bioscience
|June 30, 2016
PubMed
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Machine vision accurately monitors pig lying behavior using image processing and neural networks. This non-intrusive method precisely quantifies pig behavior for improved animal welfare.

Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Monitoring pig lying behavior is crucial for animal health and welfare.
  • Traditional methods can be intrusive and labor-intensive.
  • Advancements in machine vision offer non-intrusive monitoring solutions.

Purpose of the Study:

  • To develop and test a machine vision-based system for automatically classifying pig group lying behavior.
  • To assess the feasibility of using image processing and neural networks for this task.
  • To provide a precise method for quantifying pig lying behavior in welfare investigations.

Main Methods:

  • Utilized top-view cameras to monitor pigs over 15 days on a commercial farm.
  • Applied image processing techniques, specifically Delaunay triangulation (DT), to analyze lying patterns.
Keywords:
Delaunay triangulationanimal welfareartificial neural networklying patternpig

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  • Developed and trained a multilayer perceptron (MLP) neural network using DT features to classify lying behavior into thermal categories.

    • Main Results:

    • Defined lying patterns (close, normal, far) based on DT triangle perimeters.
    • Calculated DT features (mean perimeter, side lengths) as inputs for the MLP classifier.
    • Achieved high overall accuracy (95.6%) in classifying pig lying behavior into three thermal categories using the MLP network.

    Conclusions:

    • A combination of image processing, MLP classification, and mathematical modeling provides a precise method for quantifying pig lying behavior.
    • This machine vision approach can significantly contribute to animal welfare investigations.
    • The developed system offers a fast and non-intrusive way to monitor pig behavior.