Abstract:
Individual cattle behaviour monitoring is a promising
way of improving cattle farm management by detecting
health issues and anomalies in behaviour patterns. Accelerometer sensors are non-invasive, low-cost devices that
track daily activities and behaviour. For this, a hardware
setup is attached to the neck collar of the cow to record its
behaviour. We proposed an efficient data labelling method
to classify simultaneously occurring activities with a single
inertial sensor and a temperature sensor. Then a Machine
Learning (ML) model is trained to predict the cattle activities based on different time and frequency domain-based
statistical features. The proposed method shows an accuracy of 86% for Random Forest classifier. The behavioural
analysis of an individual cow is sent to the user interface.
The application provides visual data representation to monitor multiple cows daily and weekly.
