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Bombadil: A Foundation Model for Plant Electrophysiology

James Kirk, Piotr Bednarski, Filip Twardy, Justin Mast, Lee von Kraus · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26597 · cited by 0 works (OpenAlex) · open-access PDF

German

Lebende Pflanzen, wie Tiere, verwenden elektrische Signale, um Informationen über ihren physiologischen Zustand in ihrem Körper, von den Wurzeln bis zu den Zweigen, zu transportieren. Diese Signale können aufgezeichnet werden, wie sie bei Tieren sind, von einem Paar von differenzellen extrazellulären Elektroden, die in das Signalleitungsgewebe eingeführt werden. Während maschinelle Lernmodelle trainiert wurden, um spezifische Pflanzenstresszustände aus diesen Signalen vorherzusagen, wurde jedes dieser Modelle nur für eine einzige Aufgabe trainiert und keines wurde entworfen, um Aufgaben, Arten oder Umgebungen zu übertragen. Diese Ansätze erlauben keine einfache Skalierung über das breite Spektrum von Arten und Umgebungen, die für Landwirtschaft, Forstwirtschaft

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Plant species, Plant life, Task (project management), Engineering, Foundation (evidence), Range (aeronautics)

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