Harnessing technology for livestock research : An online sheep behavior monitoring system.

Abstract

Sheep production in extensive conditions faces several challenges. These challenges could be addressed with behavior monitoring systems, contributing to animal well-being, enhancing animal research, and improving productivity. This article presents the design, manufacture, and test of an online sheep behavior monitoring system for extensive conditions. It comprises a wearable electronic collar device and a cloud server (deployed with Amazon Web Services) for storing data and providing a web user interface. The collar has an Icarus Internet of Things (IoT) Board, allowing motion data collection with a three-axis accelerometer, global navigation satellite system (GNSS) location data acquisition, and narrowband IoT communication. The device has solar panels and a battery. Our application acquires accelerometer data at 25 Hz, location data every 10–30 s, and battery level and cellular signal strength every 50 s. We encoded accelerometer samples to reduce the transmitted data. We manufactured 30 collars that collect and transmit data to the cloud server. Our system facilitates data processing, both collar and server side. We introduce a preliminary Random Forest algorithm for behavior classification on the device that identifies “still,” “walking,” and “running” with a 78% general accuracy. The device's autonomy exceeds ten days in continuous operation (streaming raw and processed data) while if the device transmits only processed data and GNSS data every 4 h, autonomy rises to 100 days. This allows us to glimpse the application of this system in long-term research experiments and farming production.