The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system

• The study aimed at modelling cow feeding and standing behaviour detectors.
• Methods of carrying out training, testing and validation phases were outlined.
• In training and execution phase image enhancement techniques were not required.
• Great classifiers' ability to detect cows even when noise affects processed images.
• The two detectors can be used to compute Cow Feeding Index and Cow Standing Index.

Changes in cow behaviour may occur in relation to health disorders. In literature the suitability of using behavioural changes to provide an early indication of disease is studied. The possibility of achieving a real-time analysis of a number of specific changes in behaviours, such as lying, feeding, and standing, is crucial for disease prevention.Cow feeding and standing behaviour detectors were modelled and validated by defining a methodology based on the Viola–Jones algorithm and using a multi-camera video-recording system to obtain panoramic top-view images of an area of the barn.Assessment of the detection results was carried out by comparison with the results generated by visual recognition. The ability of the system to detect cow behaviours was shown by the high values of its sensitivity achieved for the behaviours of feeding and standing which were about 87% and 86%, respectively. Branching factor values for the two behaviours showed that one false positive was detected for every 13 and 6 well-detected cows, respectively. On the basis of these research outcomes, the proposed system is suitable for computing cow behavioural indices and the real-time detection of behavioural changes.