# Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels

arXiv: https://arxiv.org/abs/2509.24992 · HTML: https://mlsanity.com/paper/2509.24992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 29 Sep 2025.

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

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [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)

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 PhytoNode platform, records electric differential potential signals from 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.