【目的】林木育种研究正由经验驱动向数据与模型驱动转变,亟需构建表型解析、多组学互作建模和育种决策支撑相衔接的方法体系。围绕林木智慧育种中表型变量构建与复杂性状互作解析问题,梳理表型解析和多组学互作算法的技术路径,分析其在智慧育种体系中的作用及发展方向。【方法】从多源感知、数据处理、智能解析、结构性表型变量等角度出发,论述林木表型解析的一般过程,以及它与遗传分析、基因组选择、育种决策的关系;其次,就遗传因素、环境因素、分子调控、发育过程中共同影响林木复杂表型形成的机理进行总结,归纳出多组学数据整合、统一表示学习、基因型-环境互作(G×E)建模、复杂性状预测、机制解析等关键技术手段,并讨论多组学大模型、科研智能体、数字孪生对知识管理、任务安排和育种计划制定的作用。【结果】林木智慧育种的重点不是单一的模型预测准确度高低,而是多源数据采集、表型变量提取、相互作用建模、最终解释反馈整个过程。目前林木智慧育种还存在数据标准化程度低、小样本差异高、难以适应新环境,缺少良好可解释性及有效闭环验证等问题。【结论】未来应加快建设长期、多地点、多尺度的表型-基因型-环境协同数据底座,发展适合林木长周期和强环境异质场景的多模态基础模型、因果推断方法和人机协同智能体系统。在此基础上,形成贯通数据获取、性状解析、互作建模、方案生成和育种验证的林木智慧育种新体系。

【Objective】Forest tree breeding is shifting from experience-driven selection to data- and model-driven improvement, creating a need for an integrated framework that links phenotypic analysis, multi-omics interaction modeling, and breeding decision support. This paper focuses on phenotypic variable construction and complex-trait interaction analysis in smart forest tree breeding, reviews the technical pathways of phenotypic analysis and multi-omics interaction algorithms, and discusses their roles and future directions in intelligent breeding systems.【Method】We first discuss the general workflow of forest tree phenotypic analysis—covering multi-source sensing, data processing, intelligent analysis, and the construction of structural phenotypic variables—and elucidate its interrelationships with genetic analysis, genomic selection, and breeding decision-making. Subsequently, addressing the mechanisms by which genetic factors, environmental factors, molecular regulation, and developmental processes collectively shape complex forest tree phenotypes, we summarize key technical approaches such as multi-omics data integration, unified representation learning, genotype-environment interaction (G×E) modeling, and the prediction and mechanistic analysis of complex traits. Furthermore, we explore the roles that large multi-omics models, scientific research agents, and digital twins play in knowledge management, task scheduling, and the formulation of breeding plans.【Result】Based on the aforementioned findings, we conclude that the primary focus of smart forest tree breeding lies not merely in the predictive accuracy of isolated models, but rather in the comprehensive end-to-end process encompassing multi-source data acquisition, phenotypic variable extraction, interaction modeling, and ultimately interpretive feedback. Currently, smart forest tree breeding faces several challenges, including low levels of data standardization, issues related to small sample sizes coupled with high variability, difficulties in adapting to novel environments, a lack of robust interpretability, and an absence of effective closed-loop validation mechanisms.【Conclusion】In the future, it is necessary to accelerate the construction of a long-term, multi-site, and multi-scale collaborative phenotypic-genotypic-environmental data infrastructure, and to develop multimodal foundation models, causal inference methods, and human-machine collaborative agent systems suited to the long life cycle and strong environmental heterogeneity of forest trees. On this basis, a smart forest tree breeding system can be established that integrates data acquisition, trait analysis, interaction modeling, strategy generation, and breeding validation.