Behaviour classification of extensively grazed sheep using machine learning

The application of accelerometer sensors for automated animal behaviour monitoring is becoming increasingly common. Despite the rapid growth of research in this area, there is little consensus on the most appropriate method of data summation and analysis. The objective of this current study was to explore feature creation and machine learning (ML) algorithm options to provide the most accurate behavioural classification from an earborne accelerometer in extensively grazed sheep. Nineteen derived movement features, three epochs (5, 10 and 30 s) and four ML-algorithms (Classification and Regression Trees (CART), Linear Kernel Support-Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)) were assessed. Behaviour classification was also evaluated using three different ethograms, including detection of (i) grazing, lying, standing, walking; (ii) active and inactive behaviour; and (iii) body posture. Detection of the four mutually-exclusive behaviours (grazing, lying, standing and walking) was most accurately performed using a 10 s epoch by an SVM (76.9%). Activity was most accurately detected using a 30 s epoch by a CART (98.1%). LDA and a 30 s epoch was superior for detecting posture (90.6%). Differentiation relied on identification of disparities between behaviours rather than pattern recognition within a behaviour. The choice of epoch and ML algorithm will be dependent on the application purpose, with different combinations of each more accurate across the different ethograms. This study provides a crucial foundation for development of algorithms which can identify multiple behaviours in pasture-based sheep. This knowledge could be applied across a number of contexts, particularly at times of change in physiological or mental state e.g. during parturition or stress-inducing husbandry procedures.