Bombadil: A Foundation Model for Plant Electrophysiology
Abstract
Living plants, like animals, use electrical signals to carry information about their physiological state across their body, from roots to branches. These signals can be recorded, as they are in animals, from a pair of differential extracellular electrodes inserted into signal-conducting tissue. While machine-learning models have been trained to predict specific plant stress states from these signals, each of these models has been trained for only a single task and none have been designed to transfer across tasks, species, or environments. These approaches do not allow easy scaling across the wide range of species and environments that are relevant to agriculture, forestry, and horticulture for both commercial and consumer plant care. Here we present Bombadil, a foundation model for plant electrophysiology, pretrained without labels on over 4 million hours of electrical signals recorded from 100 plant species in ten distinct environments, including laboratories, greenhouses, commercial orchards, and natural forests. In this way, Bombadil is able to learn the fundamental features of plant electrical signals across a wide array of species and environments. Then, from those same electrical signals, Bombadil tracks changes in seven physiological indicators of plant stress, even among species and stressors with no labels in training.
Related
- Creator 1.0: Phylogeny-Guided Stagewise Pretraining for Plant Genome Foundation Models across 1,000 Species
- PlantGeneAnn: a high-accuracy ab initio plant gene annotation tool fine-tuned from a genome foundation model
- Interpreting Short Tandem Repeat Variations with Plant Genomic Foundation Models in Olive
- BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era
- Chronic Electrophysiology Captures Progressive Variability in DNA-Damaged Cardiac Networks
BibTeX
@misc{kirk2026bombadil,
title = {{Bombadil: A Foundation Model for Plant Electrophysiology}},
author = {James Kirk and Piotr Bednarski and Filip Twardy and Justin Mast and Lee von Kraus},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26597},
eprint = {2610.26597},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26597},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bombadil: A Foundation Model for Plant Electrophysiology
AU - James Kirk
AU - Piotr Bednarski
AU - Filip Twardy
AU - Justin Mast
AU - Lee von Kraus
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26597
UR - https://arxiv.org/abs/2610.26597
AB - Living plants, like animals, use electrical signals to carry information about their physiological state across their body, from roots to branches. These signals can be recorded, as they are in animals, from a pair of differential extracellular electrodes inserted into signal-conducting tissue. While machine-learning models have been trained to predict specific plant stress states from these signals, each of these models has been trained for only a single task and none have been designed to transfer across tasks, species, or environments. These approaches do not allow easy scaling across the wide range of species and environments that are relevant to agriculture, forestry, and horticulture for both commercial and consumer plant care. Here we present Bombadil, a foundation model for plant electrophysiology, pretrained without labels on over 4 million hours of electrical signals recorded from 100 plant species in ten distinct environments, including laboratories, greenhouses, commercial orchards, and natural forests. In this way, Bombadil is able to learn the fundamental features of plant electrical signals across a wide array of species and environments. Then, from those same electrical signals, Bombadil tracks changes in seven physiological indicators of plant stress, even among species and stressors with no labels in training.
ER -