An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping

Abstract

High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them. At Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory (APPL), automated stations image hundreds of plants daily across multiple remote sensing modalities; yet, trait extraction and interpretation remain manual, expert-bound, and strictly post-hoc, making analysis, not acquisition, the binding constraint on discovery. We present an end-to-end agentic AI framework that turns the facility from a data factory into an interactive autonomous, discovery platform, where scientists partner with AI agents to accelerate time to insight. A conversational Co-Scientist Agent translates a scientist's natural-language question into a structured analysis plan, and a headless Compute Agent dispatches Vision Transformer segmentation and trait extraction on the Frontier exascale supercomputer. The two agents run in separate security and resource domains and communicate over a secure, token-authenticated streaming channel, a design that accounts for the federation, data-movement, and provenance realities cloud-native agentic frameworks ignore, ensuring end-to-end provenance is captured for every interaction. The framework turns a days- to weeks-long analysis process into an interactive loop where agents reason over results, recommend next analyses, and respond to follow-up questions in seconds.

Plant phenotyping relevance

植物画像からの形質抽出を自動化するエージェント型AIフレームワークの開発であり、解析・抽出手法が研究の中心である。

Code and data availability

The supplied blocks describe the APPL agentic phenotyping framework, its ViT segmentation model, and trait datasets, but contain no public deposit, availability statement, or authors' URL for any paper-specific dataset, imagery, code, or model checkpoint. The only URLs present are generic: the DOE Public Access Plan, a

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