Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24992 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916977038065664 |
|---|---|
| author | Aust, Till Heck, Christoph Karl Buss, Eduard Hamann, Heiko |
| author_facet | Aust, Till Heck, Christoph Karl Buss, Eduard Hamann, Heiko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels Aust, Till Heck, Christoph Karl Buss, Eduard Hamann, Heiko Emerging Technologies Machine Learning 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. |
| title | Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels |
| topic | Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2509.24992 |