Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence

arXiv:2609.34567 · cs.CV, cs.ET · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.ET

Submitted: 2026-09-28

Updated: 2026-09-28

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: This review examines the evolution of plant phenotyping from conventional manual trait measurement to high-throughput, robotic, and artificial intelligence-driven crop monitoring.

Terminology

Digital Twin — 62× in this paper · explained in 4 episodes
Foundation Model — 28× in this paper · explained in 3 episodes
Foundation Models — 23× in this paper · explained in 7 episodes
Transformer — 17× in this paper · explained in 2 episodes
Generalization — 12× in this paper · explained in 13 episodes
Calibration — 10× in this paper · explained in 7 episodes
Multimodal Fusion — 10× in this paper · explained in 3 episodes
Benchmark — 9× in this paper · explained in 2 episodes
Benchmarking — 8× in this paper · explained in 3 episodes
Accuracy — 7× in this paper · explained in 1 episode
Adaptation — 7× in this paper · explained in 1 episode
Explainability — 7× in this paper · explained in 3 episodes
Agentic AI — 6× in this paper · explained in 1 episode
Agentic Reasoning — 6× in this paper · explained in 1 episode
Core — 6× in this paper · explained in 2 episodes
Grounding — 5× in this paper · explained in 3 episodes
Multimodal Reasoning — 5× in this paper · explained in 5 episodes
Center for Autonomous Robotic Systems — 3× in this paper
Data Fusion — 3× in this paper · explained in 3 episodes
Representation Learning — 3× in this paper · explained in 2 episodes
Robustness — 3× in this paper · explained in 5 episodes
Uncertainty Quantification — 3× in this paper · explained in 8 episodes
Active Learning — 2× in this paper · explained in 6 episodes
Agentic System — 2× in this paper · explained in 2 episodes
Alignment — 2× in this paper · explained in 1 episode
Consistency — 2× in this paper · explained in 3 episodes
Domain Adaptation — 2× in this paper · explained in 5 episodes
Edge AI — 2× in this paper · explained in 2 episodes
Human-in-the-Loop — 2× in this paper · explained in 3 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Late Fusion — 2× in this paper · explained in 2 episodes
Transformer Architecture — 2× in this paper · explained in 2 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Hallucination — 1× in this paper · explained in 3 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Multimodal Integration — 1× in this paper · explained in 2 episodes
Point Clouds — 1× in this paper · explained in 2 episodes
Segmentation Masks — 1× in this paper · explained in 2 episodes
Temporal Reasoning — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transformer Models — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

Abstract

This review examines the evolution of plant phenotyping from conventional manual trait measurement to high-throughput, robotic, and artificial intelligence-driven crop monitoring. Despite significant advances in imaging, autonomous platforms, multimodal sensing, and deep learning, current phenotyping systems remain fragmented across sensing modalities, crop traits, growth stages, environments, and management objectives. We therefore frame phenotyping as an integrated seed-soil-plant-environment-management (SSPEM) intelligence problem, where crop performance reflects interactions among seed quality, root-zone conditions, plant development, environmental exposure, and management actions. The review synthesizes conventional, high-throughput, robotic, and AI-driven phenotyping approaches, highlighting their capabilities and persistent limitations in temporal integration, multimodal reasoning, biological interpretation, and actionable decision support. Building on this analysis, we introduce a conceptual PhenoAgent framework that extends phenotyping beyond the estimation of isolated traits to evidence-based crop-state interpretation, uncertainty-aware reasoning, and management-oriented support. The PhenoAgent concept primarily brings together scattered advances in phenotyping to deliver insights ranging from detailed to high-level, such as what is happening in the crop, why it might be occurring, what evidence is missing, and what actions or additional measurements should be considered. We also discuss challenges in dataset scarcity, annotation, benchmarking, model generalization, and explainability. By linking multimodal phenotyping with agentic AI and closed-loop decision support, this review outlines a path to interpretable, scalable, and deployment-oriented crop intelligence.

Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence

Terminology

Abstract

Sources

Related papers