Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels

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 \textit{PhytoNode} platform, records electric differential potential signals from \textit{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. Data repository for our paper "Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels", submitted to the IEEE Sensors 2025 in Canada, Vancouver. For more information, please refer to the paper.本研究提出一种生物混合式环境传感系统，该系统将天然植物与嵌入式深度学习（embedded deep learning）相结合，可在设备端实时检测温度与臭氧浓度变化。本系统基于低功耗PhytoNode平台，采集常春藤（Hedera helix）的电位差信号，并通过嵌入式深度学习模型在设备端完成信号处理。实验结果表明，本传感装置对温度与臭氧变化具有优异的检测灵敏度，最高可达0.98。每日植株间差异以及有限的检测精度，均可通过引入额外训练数据得到缓解；此类新增数据可便捷集成至本研究的数据驱动框架中。本研究方案还具备拓展潜力，可适配更多环境监测因子与植物物种。通过在生物传感装置中集成嵌入式深度学习技术，本研究为连续环境监测乃至其他应用领域提供了一种新型低功耗解决方案。 本论文《用于检测高温与臭氧浓度升高的生物混合植物传感器的嵌入式深度学习》（"Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels"）已提交至加拿大温哥华举办的2025年IEEE传感器大会（IEEE Sensors 2025），本数据集即对应该论文。如需了解更多信息，请参阅该论文。