Oestrus detection in dairy cows from activity and lying data using on-line individual models

Automated monitoring and detection of oestrus in dairy cows is attractive for reasons of economy in dairy farming. While high performance detection has been shown possible using high-priced progesterone measurements, detection results were less reliable when only low-cost sensor data were available. Aiming at improving detection scheme reliability with the use of low-cost sensor data, this study combines information from step count and leg tilt sensors. Introducing a lying balance for the individual animal, a novel change detection scheme is derived from observed distributions of the step count data and the lying balance. Detection and hypothesis testing are based on generalised likelihood ratio optimisation combined with time-wise joint probability windowing based on the duration of oestrus and oestrus intervals. It is shown to be essential that cow-specific parameters and test statistics are derived on-line from data to cope with behaviours of individuals. Performance is validated on 18 sequences of data where definite proof of prior oestrus was available in form of subsequent pregnancy. These data were extracted from data sequences from 44 dairy cows over an 8 months period. The results show sensitivity 88.9% and error rate 5.9.%, which is very satisfactory when only cheap sensor data are used.

Research highlights▶ Detection of oestrus in dairy cows is reported using novel algorithms and a lying-balance indicator. ▶ Low-cost sensor information (step count and leg tilt) are combined to obtain efficient detection with high sensitivity (89%) and low error rate (6%). ▶ Algorithms are provided in the paper. ▶ They are fully recursive, prior training and learning are not required. ▶ Ground truth for oestrus is insemination that led to subsequent pregnancy. ▶