Design and implementation of a low-cost automatic bee productivity monitoring system.

Abstract

Despite the potential to produce 500,000 tonnes of honey annually, Uganda only produces 1% of this capacity. This low productivity is attributed to rudimentary practices such as manual beehive inspection, and negligence of bee health and productivity monitoring. To address these challenges, we set out to develop a system that supports accurate and automated monitoring of beehives.

The proposed platform comprises an Internet of Things (IoT) system, with sensors that measure the ambient conditions within a beehive (humidity, temperature) and weight. We chose these parameters because weight is an indicator of the flow of nectar into the hive, the fullness of the honey supers and occurrence of swarming events. On the other hand, the temperature is an indicator of bee brood and colony health, while humidity is an indicator of reproduction rate.

Our work focused on designing and developing the system hardware and software based on the Raspberry Pi Platform and piloting the system in the field. We achieved a functional and reliable prototype of the system that facilitates remote monitoring of the target parameters through a web-based platform. The system can be improved through integration of sound and image data and use of machine learning algorithms for predictive analytics on bee health toward pre-emptive action by the farmer.