IoT at the Grassroots – Exploring the Use of Sensors for Livestock Monitoring

Abstract

In this work we explore the use of cheap sensors to monitor the activities of dairy cattle with the aim of using these sensors as part of a system to detect important events such as when a cow is ill or on heat. This draws on advances in human activity recognition (HAR) where wearable sensors are used to collect data and infer human activity. Our sensor system is based on the Raspberry Pi microprocessor interfaced to an accelerometer sensor. We explore the use of simple machine learning techniques to infer activity from the data we collect and show that our simple system has the potential to detect different animal activities such as walking, standing and feeding. We also test the system on detection of human activities collected under controlled conditions to demonstate the potential use of the system. We envision an internet of things (IoT) system with cows in a herd mounted with appropriate sensors which relay information to servers over the internet. Farmers are then able to access information about their cattle at any time and take appropriate action when events of interest are detected.