Bombadil foundation model detects plant stress across species and environments
Bombadil: A Foundation Model for Plant Electrophysiology
Machine Learning
Summary
Plants use electrical signals to communicate changes inside their bodies, much like animals do. The authors developed Bombadil, a large AI model trained on millions of hours of plant electrical data from many species and environments without any labels. Bombadil can understand these signals well enough to track different types of stress in plants, even for species and stress types it was never specifically taught about. This approach could help monitor plant health more broadly and accurately across agriculture and natural settings.
What this means in practice
- •For agronomists: Use Bombadil to monitor multiple stress indicators on a variety of crop species and environments with a single AI model.
- •For forest managers: Detect physiological stress in different tree species across natural forest environments using one pretrained model.
Authors
James Kirk, Piotr Bednarski, Filip Twardy, Justin Mast, Lee von Kraus
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.