Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals.

In the WatchPlant project, we explore the potential of using a decentralized network of living plants as air-quality sensors by analyzing their electrophysiological responses—a concept known as phytosensing. We conducted in-lab experiments in which we expose ivy (Hedera helix) plants to ozone, an important pollutant to monitor, and measured their electrophysiological response. Because there is no well established automated way of detecting ozone exposure in plants, we propose a generic automatic toolchain that selects a high-performance subset of features and highly accurate models for plant electrophysiology. This approach derives plantand stimulus-generic features from the electrophysiological signal using the tsfresh library. Based on these features, we automatically select and optimize machine learning models using naive AutoML. We use forward feature selection to increase model performance. We show that our approach successfully classifies plant ozone exposure with accuracies of up to 94.6% on unseen data. We also show that our approach can be used for other plant species and stimuli. Our toolchain automates the development of monitoring algorithms for plants as pollutant monitors.

Till Aust studied Robotics and Autonomous Systems at the University of Lübeck, where he earned both his Bachelor's and Master's degrees. He then joined the Cyber-Physical Systems Group led by Prof. Heiko Hamann as a PhD student, focusing on the analysis of physiological signals in bio-hybrid systems. His research contributes to the EU-funded projects WatchPlant and ChronoPilot, where he explores methods to interpret physiological data for controlling bio-hybrid systems and enabling feedback mechanisms.