Edwards S

Nasirahmadi A

Sturm B

Environmental factors provide important information for the better management of pig farms and they have significant effects on pigs’ production efficiency, health and welfare. Due to the physiological and morphological limitations on thermoregulation of pigs, they change their lying behaviour to adapt to high and low temperatures. In hot conditions they avoid physical contact with others in the pen during resting time and vice versa. Visual monitoring of pig behaviours, usually practiced in small scale farms, is unreliable, expensive and time consuming in large scale farms. The development of image analysis systems could be a reliable and non-intrusive technique for automatic assessment of pig group behaviour. The aim of this study was to develop an algorithm for identification of pig group lying behaviour under commercial farm conditions using an optical method. Pigs were monitored by a top view CCD camera and animals were extracted from their background using image processing algorithms. The x–y coordinates of each binary image were used for ellipse fitting algorithms to localize each pig. As a result, ellipse parameters such as “Major axis length”, “Minor axis length” and “Centroid” could be calculated for all fitted ellipses. In order to determine the group lying behaviour, the Delaunay triangulation algorithm was applied. By means of the region properties and perimeter of each Delaunay triangule it was possible to automatically find the changes in lying behaviour of grouped pigs and the distance between pigs with high accuracy.</em

Automatic detection of changes in pig group lying behaviour using image analysis

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