Summary
Measuring plant growth and health usually involves many separate methods and tools, making it hard to get a complete picture. The authors review how plant monitoring has moved from manual checks to using robots, sensors, and AI, but note that systems still often work in isolated parts. They suggest treating plant monitoring as a holistic task that includes seeds, soil, plants, environment, and farming actions together. Their new idea, called PhenoAgent, aims to unify scattered sensing methods into a smart system that can better understand crops, explain what’s happening, and suggest useful next steps for farmers.
What this means in practice
- •For precision agriculture teams: Integrate diverse sensor data and AI reasoning into centralized crop monitoring systems that inform management decisions in real time.
- •For environmental monitoring operators: Adopt multimodal phenotyping methods to improve understanding of plant-environment interactions for ecosystem health assessment.
A survey. It maps existing work.
Authors
Muhammad Owais, Ehtesham Iqbal, Samee Ullah Khan, Muhammad Umraiz, Yusra Abdulrahman, Irfan Hussain
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 \emph{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.