PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping
This addresses the challenge of high technical barriers for users without computational expertise in plant biology, though it appears incremental as it applies existing AI methods to this domain.
The authors tackled the problem of complex and difficult-to-use plant phenotyping systems by introducing PhenoAssistant, an AI-driven system that uses natural language interaction to streamline phenotyping tasks, and validated it through case studies and evaluation tasks.
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.