用温湿度气压差检测蜂王，低功耗边端实时识别
Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
- 融合蜂巢内外温湿度气压差，构建轻量多模态检测模型
- 在STM32上实现量化决策树推理，99%准确率且无需音频数据
- 适合大规模养蜂场部署，无需人工干预的可持续监测
蜂王存在对蜂群健康与稳定至关重要，但现有监测方法依赖人力巡查，费时费力且干扰大，难以规模化。尽管近期基于音频的方法展现潜力，却常需高功耗、复杂预处理，且易受环境噪声影响。为此，本文提出一种轻量级多模态系统，通过融合蜂巢内外的温度、湿度和压力差实现蜂王检测。系统采用量化决策树推理，在商用STM32微控制器上实现低功耗实时边缘计算，不损失精度。实验表明，仅使用环境参数即可达到超过99%的检测准确率，音频特征未带来显著性能提升。本工作为非侵入式蜂巢监控提供可扩展、可持续的解决方案，推动基于现成节能硬件的自主精准养蜂发展。
原文摘要 · Abstract (English)
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.
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