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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

Japanese

生きた植物は、動物のように、電気信号を使用して、彼らの体全体の生理学的状態についての情報を運ぶために、根から枝まで、これらの信号を記録することができます。これらの信号は、動物のように、信号伝導組織に挿入する異なった電極の二つから記録することができます。機械学習モデルは、これらの信号から特定の植物のストレス状態を予測するために訓練されていますが、これらのモデルは、単一の任務のために訓練されており、これらのアプローチは、タスク、種、または環境を介して転送するように設計されていません。これらのアプローチは、農業、林業、および園芸に関連する幅広い種や環境を容易にスケールすることを許可しません。ここでは、我々は

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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