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

arXiv: https://arxiv.org/abs/2412.13312 · HTML: https://mlsanity.com/paper/2412.13312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response](https://mlsanity.com/paper/1611.09820/) (2016)
- [Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response](https://mlsanity.com/paper/1707.07620/) (2017)

## Built on by (most cited first, 2 of 2)

- [When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems](https://mlsanity.com/paper/2506.23872/) (2025)
- [Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels](https://mlsanity.com/paper/2509.24992/) (2025)

## Abstract (arXiv)

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.