When raising livestock, feeding and associated behaviours need to be accurately quantified in order to detect any health and welfare problems at an early stage. Changes in feeding behaviours are a sign of such problems, and even subtle differences in the way an animal consumes its food could help in spotting health and welfare issues in livestock. Researchers supported by the EU-funded HealthyLivestock and Feed-a-Gene projects have developed a promising new method for monitoring pig feeding and foraging that could help with the early detection of such problems. Described in a paper published in the ‘Biosystems Engineering’ journal, the automated detection method can be used in a variety of husbandry and management situations. Based on convolutional neural networks, the 2D camera-based deep learning method automatically detects pig feeding behaviour without the use of additional sensors or individual marking. According to the study, “the system operates on grayscale video images, and was trained to handle the constantly changing farm conditions, e.g. lighting conditions, problems of occlusion caused by other pigs, and insects occluding the image from the camera.” Feeding behaviours aren’t estimated using traditional pig tracking methods. Instead, the researchers used “GoogLeNet-like architectures … to monitor a smaller predefined pen area covering two food troughs and a simple, clearly defined area in front of those troughs. In this way, the proposed system avoids short ID track-related issues, which can continuously distort the accumulative feeding-behaviour recognition process.”

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