植物学报2026，Vol.61Issue(4)：555-570,16.DOI:10.3724/CBB-2026-0138
人工智能驱动的植物科学跨尺度融合研究:进展、挑战与展望
Artificial Intelligence-driven Multi-scale Research in Plant Science:Progress,Challenges,and Perspectives
摘要
Abstract
The rapid development of multi-omics technologies,high-throughput phenotyping technologies,and agricul-tural and ecosystem observation networks has generated massive,heterogeneous,and multi-scale datasets,spanning multiple layers of plant biological systems.Integrating these heterogeneous data to decipher complex biological proc-esses,and enable accurate prediction has become a major challenge in modern plant science.In recent years,artificial intelligence(AI)has emerged as a powerful approach for multi-omics integration,gene function prediction,plant pheno-typing,and modeling of complex biological processes,providing powerful tools and new perspectives for plant research.This review systematically summarizes recent advances in AI applications across multiple organizational levels of plant systems,covering molecular regulation,trait formation,environmental adaptation,agricultural production,and ecosystem research.Emphasis is placed on the roles of AI in cross-scale data integration,deciphering complex biological processes,and predictive modeling.We further discuss current challenges,including data quality and sharing,model interpretability,cross-scale coupling,and insufficient mechanistic understanding.Looking forward,AI frameworks that integrate data-driven approaches with biological knowledge and mechanistic models are expected to advance plant science from descriptive and correlative analyses toward mechanistic understanding,functional prediction,and intelligent design,thereby providing a stronger theoretical and technological foundation for basic plant research,smart agriculture,and the sustainable utilization of plant resources.关键词
人工智能/植物科学/多组学/跨尺度融合/数字植物Key words
artificial intelligence/plant science/multi-omics/cross-scale integration/digital plant引用本文复制引用
郑聪聪,高树琴,郭晓玉,王楠,马慧敏,钟上威,景海春,张玉成..人工智能驱动的植物科学跨尺度融合研究:进展、挑战与展望[J].植物学报,2026,61(4):555-570,16.基金项目
中国科学院战略性先导科技专项(No.XDA0450203,No.XDB1290000)、中国科学院科研项目和国家重点研发计划(No.2022YFF1003200) （No.XDA0450203,No.XDB1290000）
