A conversational multi-agent AI system for automated plant phenotyping

Feng Chen, Ilias Stogiannidis, Andrew Wood, Danilo Bueno, Dominic Williams, Fraser Macfarlane, Bruce D. Grieve, Darren Wells, Jonathan A. Atkinson, Malcolm J. Hawkesford, Stephen A. Rolfe, Tracy Lawson, Tony Pridmore, Sotirios A. Tsaftaris, Mario Valerio Giuffrida

Nature Communications (2026)

Chen, F., Stogiannidis, I., Wood, A. et al. A conversational multi-agent AI system for automated plant phenotyping. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71090-y

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@Article{Chen2026,
author={Chen, Feng
and Stogiannidis, Ilias
and Wood, Andrew
and Bueno, Danilo
and Williams, Dominic
and Macfarlane, Fraser
and Grieve, Bruce D.
and Wells, Darren
and Atkinson, Jonathan A.
and Hawkesford, Malcolm J.
and Rolfe, Stephen A.
and Lawson, Tracy
and Pridmore, Tony
and Tsaftaris, Sotirios A.
and Giuffrida, Mario Valerio},
title={A conversational multi-agent AI system for automated plant phenotyping},
journal={Nature Communications},
year={2026},
month={Apr},
day={03},
abstract={Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.},
issn={2041-1723},
doi={10.1038/s41467-026-71090-y},
url={https://doi.org/10.1038/s41467-026-71090-y}
}

Abstract

Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.