mlsanityData to 14 Oct 2026

PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping

arXiv 2504.19818 ↗First posted 28 Apr 2025cs.MA cs.AIcited by 4

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 32 of its 83 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

32 of 83 references.

References · 32

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026ToolRosella: Translating Code Repositories into Standardized Tools for Scientific Agentscited by 2
  2. 2025Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Sciencecited by 0
  3. 2026PhenoNEST: A Neuro-Symbolic Framework for Ontology-Aware Multimodal Plant Phenotyping and Trait Discoverycited by 0
  4. 2026MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classificationcited by 0

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  1. 2026An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping
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  3. 2026Plant-Inspired AI: Plants as Inspiration for Novel Problem Formulations, and Two Case Studies
  4. 2026From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping
  5. 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis
  6. 2026AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
  7. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
  8. 2026Benchmarking Vision-Language Models for Microscopic Plant Image Understanding