# When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems

arXiv: https://arxiv.org/abs/2506.23872 · HTML: https://mlsanity.com/paper/2506.23872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [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)
- [Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals](https://mlsanity.com/paper/2412.13312/) (2024)

## Built on by (most cited first, 1 of 1)

- [Early Detection of Water Stress by Plant Electrophysiology: Machine Learning for Irrigation Management](https://mlsanity.com/paper/2604.28038/) (2026)

## Abstract (arXiv)

Living plants, while contributing to ecological balance and climate regulation, also function as natural sensors capable of transmitting information about their internal physiological states and surrounding conditions. This rich source of data provides potential for applications in environmental monitoring and precision agriculture. With integration into biohybrid systems, we establish novel channels of physiological signal flow between living plants and artificial devices. We equipped *Hedera helix* with a plant-wearable device called PhytoNode to continuously record the plant's electrophysiological activity. We deployed plants in an uncontrolled outdoor environment to map electrophysiological patterns to environmental conditions. Over five months, we collected data that we analyzed using state-of-the-art and automated machine learning (AutoML). Our classification models achieve high performance, reaching macro F1 scores of up to 95 percent in binary tasks. AutoML approaches outperformed manual tuning, and selecting subsets of statistical features further improved accuracy. Our biohybrid living system monitors the electrophysiology of plants in harsh, real-world conditions. This work advances scalable, self-sustaining, and plant-integrated living biohybrid systems for sustainable environmental monitoring.