Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals

TL;DR

This paper presents a novel automated system that uses electrophysiological signals from plants to classify ozone exposure with high accuracy, advancing phytosensing technology for urban air quality monitoring.

Contribution

It introduces an automated feature selection and machine learning pipeline for plant electrophysiology data to detect ozone exposure accurately.

Findings

Achieved up to 94.6% classification accuracy on unseen data.

Demonstrated applicability across different plant species and stimuli.

Developed a generic toolchain for automating phytosensing model development.

Abstract

In our project WatchPlant, we propose to use a decentralized network of living plants as air-quality sensors by measuring their electrophysiology to infer the environmental state, also called phytosensing. We conducted in-lab experiments exposing ivy (Hedera helix) plants to ozone, an important pollutant to monitor, and measured their electrophysiological response. However, there is no well established automated way of detecting ozone exposure in plants. We propose a generic automatic toolchain to select a high-performance subset of features and highly accurate models for plant electrophysiology. Our approach derives plant- and stimulus-generic features from the electrophysiological signal using the tsfresh library. Based on these features, we automatically select and optimize machine learning models using 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. Our results help implement significant advancements for phytosensing devices contributing to the development of cost-effective, high-density urban air monitoring systems in the future.