A conversational multi-agent AI system for automated plant phenotyping

Feng Chen, Ilias Stogiannidis, Sotirios A. Tsaftaris, Mario Valerio Giuffrida

Keywords: LLMs, AI Agents, Agentic AI, AI for Science, Plant Phenotyping

Domains: AI for Science

TL;DR: PhenoAssistant is a multi-agent AI system for plant phenotyping that uses LLMs and computer vision to automate phenotype extraction, data analysis, and workflow generation through natural language interaction.

External Link: https://www.nature.com/articles/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.

Submission Number: 107