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Automatic scoring of lateral and sternal lying posture in grouped pigs using image processing and Support Vector Machine

Lookup NU author(s): Abozar Nasirahmadi, Dr Barbara Sturm, Emerita Professor Sandra Edwards

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2018 The Authors The behaviour of animals provides information on their health, welfare and environmental situation. In different climatic conditions, pigs adopt different lying postures; at higher temperatures they lie laterally on their side with their limbs extended, while in lower temperatures they will adopt a sternal or belly lying posture. Machine vision has been widely used in recent years to monitor individual and group pig behaviours. So, the aim of this study was to determine whether a two-dimensional imaging system could be used for lateral and sternal lying posture detection in grouped pigs under commercial farm conditions. An image processing algorithm with Support Vector Machine (SVM) classifier was applied in this work. Pigs were monitored by top view RGB cameras and animals were extracted from their background using a background subtracting method. Based on the binary image properties, the boundaries and convex hull of each animal were found. In order to determine their lying posture, the area and perimeter of each boundary and convex hull were calculated in lateral and sternal lying postures as inputs for training of a linear SVM classifier. The trained SVM was then used to detect the target postures in binary images. By means of the image features and the classification technique, it was possible to automatically score the lateral and sternal lying posture in grouped pigs under commercial farm conditions with high accuracy of 94.4% for the classification and 94% for the scoring (detection) phases using two-dimensional images.


Publication metadata

Author(s): Nasirahmadi A, Sturm B, Olsson A-C, Jeppsson K-H, Müller S, Edwards S, Hensel O

Publication type: Article

Publication status: Published

Journal: Computers and Electronics in Agriculture

Year: 2019

Volume: 156

Pages: 475-481

Print publication date: 01/01/2019

Online publication date: 12/12/2018

Acceptance date: 03/12/2018

Date deposited: 04/12/2018

ISSN (print): 0168-1699

ISSN (electronic): 1872-7107

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.compag.2018.12.009

DOI: 10.1016/j.compag.2018.12.009


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