Abstract
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.
Highlights
Recent developments in knowledge and new technologies have expanded the possibilities for monitoring of behaviours, health and disease of animals in large-scale farms, which can help to improve their welfare
One of the most important steps, which has a considerable effect on the result of the detection technique, is localizing each animal in the image processing
The results illustrated that the image processing technique was able to localize correctly around 92% of individual pigs in binary images
Summary
Recent developments in knowledge and new technologies have expanded the possibilities for monitoring of behaviours, health and disease of animals in large-scale farms, which can help to improve their welfare. Two-dimensional (2D) cameras and image processing methods were used by Shao et al (1998) and Shao and Xin (2008) to obtain lying behaviour changes of pigs in various thermal conditions in research barn conditions. A computer vision system based on using 3D cameras was developed by (Lao et al, 2016) to assess sow behaviours including lying, sitting and standing. In another project, standing and lying behaviours of pigs were monitored at day and night time using Kinect depth sensors (Kim et al, 2017), with results again indicating the ability to use an image processing technique as a noninvasive way to monitor standing and lying of pigs.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
