A conversational multi-agent AI system for automated plant phenotyping

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.

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Publication Details

Journal
Nature Communications
Published
2026-04-03
DOI
https://doi.org/10.1038/s41467-026-71090-y
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00

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article

A conversational multi-agent AI system for automated plant phenotyping

Jonathan A. Atkinson, Malcolm John Hawkesford, Mario Valerio Giuffrida, Fraser Macfarlane et al.
Nature Communications
Smart Agriculture and AI
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A conversational multi-agent AI system for automated plant phenotyping

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article en

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.

Nature Communications
University of Essex (GB), University of Nottingham (GB), James Hutton Institute (GB), University of Illinois Urbana-Champaign (US), Rothamsted Research (GB), Dignity Health (US), University of Manchester (GB), Carnegie Department of Plant Biology (US), University of Sheffield (GB), University of Edinburgh (GB)
UK Research and Innovation, Directorate for Biological Sciences, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council
Quality Education
Openalex Percentile: Top 32%
Smart Agriculture and AI
DOIOpen Access PDFOpenAlex
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