Design of an automated system for continuous monitoring of dairy cow behaviour in free-stall barns

Abstract

Change in cows behaviours is one of the indicators useful to help identifying when animals become ill. The need to analyse a large number of animals at a time due to the increase in the herd dimension in intensive farming has led to the use of automated systems. Among automated systems, inertial sensor-based systems have been utilised to distinguish behavioural patterns in livestock animals. In this field, the overall aim of this thesis work, which was inherent to the field of the Precision Livestock Farming, was to contribute to the improvement of the systems based on wearable sensors that are able to recognise the main behavioural activities (i.e., lying, standing, feeding, and walking) of dairy cows housed in a free-stall barn. This objective was achieved through different steps aimed at producing an advance in the state of the art. A novel algorithm, characterised by a linear computational time, was implemented with the aim to improve real-time monitoring and analysis of walking behaviour of dairy cows. The algorithm computed the number of steps of each cow from accelerometer data by making use of statistically defined thresholds. Algorithm accuracy was carried out by computing total error (E equal to 9.5 %) and Relative Measurement Error (RME between 2.4% and 4.8%). A new classifier was assessed to recognise the cow feeding and standing behavioural activities by using statistically defined thresholds computed from accelerometer data. The accuracy of the classification was assessed by computing of the Misclassification Rate (MR equal to 5.56%). A new data acquisition system assessed in a free-stall barn allowed the acquisition of data from different sensor devices, with a sampling frequency of 4 Hz, during the animals daily routine. It required a simple installation into the building and it did not need any preliminary calibration. The performance of this system was assessed by computing a Stored Data Index (DSI) that resulted equal to 83%. Finally, the overall design of an automated monitoring system based on wearable sensors was proposed.