Unverified paper record
A Conversational Multi-Agent AI System for Automated Plant Phenotyping
White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 7 Jan 2026
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.
Plant phenotyping relevance
植物フェノタイピングの自動抽出を支援するAIシステムとツールキットを開発し、ケーススタディと評価タスクで検証しているため、方法・ソフトウェアが中心です。
abstractwe introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction.
abstractPhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction
abstractWe validate PhenoAssistant through several representative case studies and a set of evaluation tasks.
Code and data availability
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