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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.
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.
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