Real-time monitoring of pig activity and behaviour recognition

T. Leroy; F. Borgonovo; A. Costa; J.M. Aerts; M. Guarino; D. Berckmans1

Real-time monitoring of pig activity and behaviour recognition

In: Precision livestock farming '09

Authors:

T. Leroy

T. Leroy ¹M3-BIORES: Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; daniel.berckmans@biw.kuleuven.be

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F. Borgonovo

F. Borgonovo ²Department of Veterinary Science and Technologies for Food Safety, Faculty of Veterinary Medicine, Università degli Studi, via Celoria 10, 20133 Milan, Italy

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A. Costa

A. Costa ²Department of Veterinary Science and Technologies for Food Safety, Faculty of Veterinary Medicine, Università degli Studi, via Celoria 10, 20133 Milan, Italy

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J.M. Aerts

J.M. Aerts ¹M3-BIORES: Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; daniel.berckmans@biw.kuleuven.be

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M. Guarino

M. Guarino
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, and

D. Berckmans1

D. Berckmans1 ¹M3-BIORES: Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; daniel.berckmans@biw.kuleuven.be

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Type:: Chapter

Pages:: 273–282

DOI:: https://doi.org/10.3920/9789086866632_034

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In close relation to production, physiology and health, behaviour is an important indicator to evaluate animal welfare. It can also provide feedback on the quality of the animal’s environment. The aim of this paper is to propose an automatic system for continuously measuring activity levels of pigs by using camera images. It was also tested whether dynamic responses (i.e. steps) in the activity level can be linked to different behaviour types. Reference data were collected by human experts who labelled the behaviour of the pigs in the video recordings, in parallel to the automatic activity monitoring system. For the experiments two adjacent pig pens in a commercial pig house were used (5.9×2.6 m), each populated by about 15 pigs. An infrared-sensitive CCD camera was mounted 5 meters above the floor of the pen. Images were captured with a resolution of 768×586 pixels at 1 Hz frame rate. Software was developed to measure the activity level of animals from the camera image. Four zones were defined in the image, each covering half a pen. Every second, the algorithm logged the camera image and the activity index for each zone. This activity index was calculated as the fraction of the floor space in the pen zone that contained motion in between two subsequent camera images. The behaviour of the animals in each zone of every recorded video image was visually labelled as one out of five possible behaviour scores: ‘no activity’, ‘fighting’, ‘biting’, ‘nuzzling’ and ‘eating’. For each of the behaviour types except the ‘no activity’ behaviour, the response of the activity index to the occurrence of the behaviour was modelled as the average and the step of the activity index within a time window between 30 seconds before and 30 seconds after the start of the behaviour. A Naïve Bayesian Classifier was trained for the behaviour types ‘fighting’, ‘biting’, nuzzling’ and ‘eating’ using the mean activity value and the size of the activity step as features, calculated from the activity level and labels for day 1. Classification was tested by automatically classifying the features calculated for day 2 and comparing with the corresponding manual labelling. Results showed a correct classification rate of 73.3% for ‘fighting’, 60% for ‘eating’, 46.2% for ‘biting’ and 24% for nuzzling. However, the behaviours ‘fighting’, ‘biting’ and ‘nuzzling’ were often misclassified as each other. The general conclusion is that the global activity level of the group can only be used to recognise behaviour types that are performed simultaneously by the majority of the animals, such as ‘eating’. It proves to be too crude to be an indicator of individual animal behaviour.

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Precision livestock farming '09

E-Book ISBN:: 9789086866632

Publisher:: Wageningen Academic

Print Publication Date:: 30 Oct 2009

Subjects
- Life Sciences
  - General
  - Animal & Veterinary

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Real-time monitoring of pig activity and behaviour recognition

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Precision livestock farming '09

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