用自然语言对话自动完成植物表型分析，降低技术门槛。
PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping
- 用大模型协调工具链，通过对话完成表型提取、可视化与模型训练。
- 在多个案例中实现端到端自动化，用户无需编程经验即可操作。
- 适合植物研究者、生物学家等非技术背景用户快速上手。
植物表型分析日益依赖（半）自动化图像分析流程以提升准确性和可扩展性。然而，现有许多方案过于复杂，难以复现和维护，对缺乏计算专业知识的用户构成高门槛。为此，我们提出PhenoAssistant：一个开创性的基于AI的对话式多智能体系统，通过直观的自然语言交互简化植物表型分析流程。PhenoAssistant利用大语言模型协调一组精选工具，支持自动表型提取、数据可视化及自动化模型训练。我们在多个代表性案例和评估任务中验证了其有效性。通过显著降低技术门槛，PhenoAssistant彰显了AI驱动方法在推动植物生物学中AI普及方面的潜力。
原文摘要 · Abstract (English)
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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