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.