Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning

Calving is one of the most critical moments during the life of a cow and their calves. Timely supervision is therefore crucial for animal welfare as well as the farm economics. In this study, we propose a framework to predict calving within 24 h, 12 h, 6 h, 3 h and 1 h of dairy cows using sequential sensor data. In particular, data were extracted from 2363 cows coming from 8 commercial farms between August 2016 and November 2020. Two sensors attached to the neck and leg of each cow measured rumination, eating, lying, standup, walking and inactive behavior on a minute basis. A novel methodology was used to impute the missing values in the sensor sequences by leveraging the observed values of all the behavioral activities recorded by the sensors. A deep learning model was then used to predict the moment of calving on an hourly basis using the imputed sensor sequences. Results show that 65% of the calvings within 24 h can be detected with a precision of 77%, while 57% of calvings occurring within 3 h can be identified with a precision equal to 49%. Moreover, we find that using the missing value imputations significantly improves the predictive performance for observations containing up to 60% of missing values. The framework proposed in this study can be used by farmers to optimize their calving management and hence improve animal monitoring.