Advancing Beekeeping: IoT and TinyML for Queen Bee Monitoring Using Audio Signals

Beekeeping plays an essential role in maintaining ecosystems through pollination and enhancing biodiversity. The presence of the queen bee inside the hive is an important indicator for the health of the bee colony. Monitoring the health of honeybees and their hives is crucial not only for bees but also for the entire ecosystem. This article introduces a tiny machine learning (ML) application for edge computing in the Internet-of-Things (IoT) systems, designed to predict the queen bee's presence. The solution, implemented on a low-power microcontroller (MCU), listens to the sound produced by honeybees and aids beekeepers by automating health assessments of the colony. The system utilizes audio recordings of honeybees combined with artificial intelligence (AI) techniques, while the second focuses on optimizing a feature extraction algorithm from these recordings to minimize latency and energy use in the IoT setup. The findings show that despite the implementation of a simpler ML model and audio preprocessing with lower computational precision, the final metrics remain comparable to those analyzed, with only a limited reduction.