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Abstract
The need of an automated support system that helps beekeepers maintain and improve beehive population was always a very stressing aspect of their work considering the importance of a healthy bee population. This paper presents a proof of concept based on Internet of Things technology, proposing a smart monitoring system using machine-learning processes and edge computing for communication and control. IoT sensors collect data and extract features from hive noises, while a TinyML network performs inference on low-power microcontroller devices for decision support. By moving inference to the edge, the system improves the autonomy of beekeeping solutions and supports healthier hive maintenance without relying on high-power cloud processing.
Relevancy to Gratheon
This paper is directly relevant to Gratheon's edge-device roadmap because it combines the same product constraints Gratheon faces in remote apiaries: low-power sensing, local audio/noise processing, and actionable hive-state inference without continuous cloud dependence. Its TinyML framing is useful for deciding which colony-health signals should run on-device and which should be uploaded to the monitoring dashboard. The proof-of-concept also provides a concrete benchmark for packaging sensor acquisition, feature extraction, and beekeeper decision support into one deployable smart-hive node.