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

Published online by Cambridge University Press:  29 June 2016

A. Nasirahmadi ,
O. Hensel ,
S. A. Edwards  and
B. Sturm
A. Nasirahmadi*
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne NE1 7RU, UK Department of Agricultural and Biosystems Engineering, University of Kassel, 34213 Witzenhausen, Germany
O. Hensel
Affiliation:
Department of Agricultural and Biosystems Engineering, University of Kassel, 34213 Witzenhausen, Germany
S. A. Edwards
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
B. Sturm
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne NE1 7RU, UK Department of Agricultural and Biosystems Engineering, University of Kassel, 34213 Witzenhausen, Germany
*
† E-mail: abozar.nasirahmadi@ncl.ac.uk
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Abstract

Machine vision-based monitoring of pig lying behaviour is a fast and non-intrusive approach that could be used to improve animal health and welfare. Four pens with 22 pigs in each were selected at a commercial pig farm and monitored for 15 days using top view cameras. Three thermal categories were selected relative to room setpoint temperature. An image processing technique based on Delaunay triangulation (DT) was utilized. Different lying patterns (close, normal and far) were defined regarding the perimeter of each DT triangle and the percentages of each lying pattern were obtained in each thermal category. A method using a multilayer perceptron (MLP) neural network, to automatically classify group lying behaviour of pigs into three thermal categories, was developed and tested for its feasibility. The DT features (mean value of perimeters, maximum and minimum length of sides of triangles) were calculated as inputs for the MLP classifier. The network was trained, validated and tested and the results revealed that MLP could classify lying features into the three thermal categories with high overall accuracy (95.6%). The technique indicates that a combination of image processing, MLP classification and mathematical modelling can be used as a precise method for quantifying pig lying behaviour in welfare investigations.

Keywords

animal welfareartificial neural networkDelaunay triangulationlying patternpig
Type
Research Article
Information
animal , Volume 11 , Issue 1 , January 2017 , pp. 131 - 139
Copyright
© The Animal Consortium 2016 

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