# Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing

arXiv: https://arxiv.org/abs/2509.14061 · HTML: https://mlsanity.com/paper/2509.14061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees](https://mlsanity.com/paper/2311.10876/) (2023)
- [UrBAN: Urban Beehive Acoustics and PheNotyping Dataset](https://mlsanity.com/paper/2406.03657/) (2024)

## Abstract (arXiv)

Queen bee presence is essential for the health and stability of honeybee colonies, yet current monitoring methods rely on manual inspections that are labor-intensive, disruptive, and impractical for large-scale beekeeping. While recent audio-based approaches have shown promise, they often require high power consumption, complex preprocessing, and are susceptible to ambient noise. To overcome these limitations, we propose a lightweight, multimodal system for queen detection based on environmental sensor fusion-specifically, temperature, humidity, and pressure differentials between the inside and outside of the hive. Our approach employs quantized decision tree inference on a commercial STM32 microcontroller, enabling real-time, low-power edge computing without compromising accuracy. We show that our system achieves over 99% queen detection accuracy using only environmental inputs, with audio features offering no significant performance gain. This work presents a scalable and sustainable solution for non-invasive hive monitoring, paving the way for autonomous, precision beekeeping using off-the-shelf, energy-efficient hardware.