  We demonstrate a working prototype for the monitoring of cow welfare by
automatically analysing the animal behaviours. Deep learning models have been
developed and tested with videos acquired in a farm, and a precision of 81.2\%
has been achieved for cow identification. An accuracy of 84.4\% has been
achieved for the detection of drinking events, and 94.4\% for the detection of
grazing events. Experimental results show that the proposed deep learning
method can be used to identify the behaviours of individual animals to enable
automated farm provenance. Our raw and ground-truth dataset will be released as
the first public video dataset for cow identification and action recognition.
Recommendations for further development are also provided.
