Wi-Fi Based Monitoring of Goat Feeding Behaviour Using Gyro- and Accelerometer Data

Goat farming shows potential as one of the solutions for providing quality livestock products in developing countries. As with humans, goats require good health to yield high-quality products. Through the proposed method, the researcher’s developed a system that uses data from gyrometer and accelerometer pairs attached to the goats’ neck. Meter data are transmitted to a base station, collated and stored in a spreadsheet file. The collected data are then analyzed by a Matlab application to determine their correlation with goat feeding behavior. The meter data are analyzed in such a way that the system can determine whether the goat is feeding or not. The system can also determine the length of time a goat is not eating and idling. In addition to the gyro-accelerometer pair, a temperature sensor is included to monitor the goats’ body temperature. Based on the results of the analysis made on collected data, in comparison with actual video footage, the system has an accuracy of 98.02% in determining the feeding pattern of goats. However, the performance of the system can further be improved by the use of other machine learning algorithms for data classification.