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AI foundation models in plant biology.

¹Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, UK.

The New Phytologist

|June 30, 2026

Summary

Foundation models (FMs), a type of artificial intelligence (AI), are revolutionizing plant science by analyzing vast datasets. These powerful AI tools accelerate discovery and automate research workflows for plant biologists.

Keywords:

AI agent artificial intelligence foundation model large language model plant genomics protein engineering single‐cell transcriptomics

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Area of Science:

Plant Biology
Bioinformatics
Artificial Intelligence

Background:

Plant biology research generates massive multi-scale, multi-modal data, posing challenges for biological interpretation.
Foundation models (FMs), large-scale AI systems trained on extensive biological data, offer a solution to decode this complexity.

Purpose of the Study:

To highlight the transformative impact of foundation models (FMs) in plant science.
To showcase diverse applications of FMs across genomics, proteomics, phenomics, and cell biology.

Main Methods:

Leveraging pre-trained FMs on large datasets of sequences, structures, and images.
Adapting FMs for specific tasks such as decoding regulatory grammar, protein engineering, phenotype scoring, and cell type annotation.
Utilizing FM-powered AI agents to automate research workflows and knowledge retrieval.

Main Results:

Genomic FMs are decoding plant gene regulation.
Protein FMs facilitate rational protein engineering.
Vision FMs enable large-scale plant phenotype analysis.
Single-cell FMs aid in cross-species cell type annotation.
AI agents powered by FMs accelerate knowledge discovery and research automation.

Conclusions:

Foundation models are a breakthrough technology in plant science, accelerating the translation of complex data into biological understanding.
While experimental validation is crucial, FMs significantly empower plant scientists to expedite scientific discovery and research efficiency.