用植物+嵌入式AI实时监测温湿度和臭氧，低功耗且可扩展。
Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels
- 用植物电位信号结合嵌入式深度学习实现本地化环境感知
- 温度与臭氧检测灵敏度最高达0.98，可适应不同植物和日常波动
- 适合生态监测、环境传感及可扩展的生物混合系统研究者
我们提出一种基于天然植物与嵌入式深度学习的生物混合环境传感器系统，用于实时、在设备端检测温度与臭氧水平变化。该系统基于低功耗PhytoNode平台，采集常春藤（Hedera helix）的电位差信号，并在设备上使用嵌入式深度学习模型进行处理。实验表明，该传感装置对温度与臭氧变化的检测灵敏度最高可达0.98。通过引入额外训练数据，可有效缓解每日变化与植株间差异带来的影响，且该数据驱动框架易于扩展。本方法具备向新环境因子与植物种类扩展的潜力。通过在生物传感设备中集成嵌入式深度学习，我们提供了一种新型低功耗持续环境监测方案，未来或可拓展至其他应用场景。
原文摘要 · Abstract (English)
We present a bio-hybrid environmental sensor system that integrates natural plants and embedded deep learning for real-time, on-device detection of temperature and ozone level changes. Our system, based on the low-power PhytoNode platform, records electric differential potential signals from Hedera helix and processes them onboard using an embedded deep learning model. We demonstrate that our sensing device detects changes in temperature and ozone with good sensitivity of up to 0.98. Daily and inter-plant variability, as well as limited precision, could be mitigated by incorporating additional training data, which is readily integrable in our data-driven framework. Our approach also has potential to scale to new environmental factors and plant species. By integrating embedded deep learning onboard our biological sensing device, we offer a new, low-power solution for continuous environmental monitoring and potentially other fields of application.
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