发表机构
Khalifa University of Science and Technology; Chung-Ang University（哈利法科学技术大学; 中央大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述提出将植物表型分析视为SSPEM智能问题，并引入PhenoAgent框架，整合多模态感知与智能体AI，以实现可解释、可扩展的作物智能决策支持。
AI 中文摘要
本综述考察了植物表型分析从传统人工性状测量到高通量、机器人化及人工智能驱动的作物监测的演变过程。尽管在成像、自主平台、多模态感知和深度学习方面取得了显著进展，但当前的表型分析系统在感知模态、作物性状、生长阶段、环境和管理目标之间仍然碎片化。因此，我们将表型分析构建为一个集成的“种子-土壤-植物-环境-管理”（SSPEM）智能问题，其中作物表现反映了种子质量、根区条件、植物发育、环境暴露和管理行为之间的相互作用。本综述综合了传统、高通量、机器人化和AI驱动的表型分析方法，强调了它们在时间整合、多模态推理、生物学解释和可操作决策支持方面的能力及持续存在的局限性。基于此分析，我们引入了一个概念性的PhenoAgent框架，将表型分析从孤立性状的估计扩展到基于证据的作物状态解释、不确定性感知推理和管理导向支持。PhenoAgent概念主要汇集了表型分析领域的分散进展，以提供从细节到高层次的洞察，例如作物中正在发生什么、可能的原因、缺失的证据以及应考虑采取哪些行动或额外测量。我们还讨论了数据集稀缺性、标注、基准测试、模型泛化和可解释性方面的挑战。通过将多模态表型分析与智能体AI和闭环决策支持相结合，本综述勾勒出一条通往可解释、可扩展且面向部署的作物智能的路径。
英文摘要
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.
