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PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping

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arxiv 2504.19818 v1 pith:JX464NYP submitted 2025-04-28 cs.MA cs.AI

Feng Chen , Ilias Stogiannidis , Andrew Wood , Danilo Bueno , Dominic Williams , Fraser Macfarlane , Bruce Grieve , Darren Wells
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Jonathan A. Atkinson Malcolm J. Hawkesford Stephen A. Rolfe Tracy Lawson Tony Pridmore Mario Valerio Giuffrida Sotirios A. Tsaftaris
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classification cs.MAcs.AI
keywords phenoassistantautomatedplantphenotypingai-drivenlanguagemodelsystem
verification ladder T0 review T1 audit T2 compute T3 formal
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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 significantly lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.

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Cited by 1 Pith paper

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  1. Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A three-agent LLM system, Aleks, autonomously chooses problem framing, features, and models for plant disease prediction, with domain knowledge and shared memory improving coherence across iterations.

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