A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method. (29th June 2016)

• Record Type: Journal Article
• Authors: Nasirahmadi, A.; Hensel, O.; Edwards, S. A.; Sturm, B.
• Is Part Of: Animal. Volume 11:Number 1(2017:Jan.)
• Page Start: 131
• Page End: 139
• Publication Date: 2016-06-29
• Subjects: animal welfare, -- artificial neural network, -- Delaunay triangulation, -- lying pattern, -- pig; Animal breeding -- Periodicals; Animal genetics -- Periodicals; Animal nutrition -- Periodicals; Animal physiology -- Periodicals; Environmental sciences -- Periodicals; 636.005
• DOI: 10.1017/S1751731116001208
• Languages: English
• ISSNs: 1751-7311

Abstract

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.