# Paper: An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping

---
type: paper
title: An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping
authors: Renan Souza, Daniel Rosendo, Kelsey Carter, John Lagergren, Frédéric Suter, Shelaine L. Curd, Gerald A. Tuskan, Rafael Ferreira da Silva, et al.
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.31831
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-30
updated_at: 2026-06-30
status: queued
relevance: high
topics:
  - agent-safety
  - planning
  - tool-use
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cs.AI
related_concepts:
  -
related_jobs:
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related_experiments:
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related_projects:
  -
collection_score: 13
collection_queries: agentic-ai, ai-agent
---

## One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

## Why Collected

- matched queries: agentic-ai, ai-agent
- inferred topics: agent-safety, planning, tool-use
- arXiv categories: cs.AI
- collection score: 13

## Review Checklist

- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?

## Links

- arXiv: https://arxiv.org/abs/2606.31831