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.
Plant science is entering a new era characterized by the rapid accumulation of multi-scale biological data and increasing demands for understanding complex biological systems.Fundamental challenges remain in uncovering the mechanisms of trait formation,deciphering multilayered regulatory networks and environmental adaptive evolution,and intelligent design breeding.Rapid advances in artificial intelligence(AI)are reshaping life science research,driving a transition from traditional experiment-and data-driven research toward a new paradigm in which data,computational models,and biological mechanisms are integrated synergistically.To highlight recent progress at this interdisciplinary frontier,we organized the Special Issue on"Integration of Plant Science and Artificial Intelligence".It gathers 12 contribu-tions spanning theory,methodology,application,and education.The contributions emphasize multi-omics integration,complex trait analysis,AI-assisted phenotyping,and smart agriculture,reflecting current methodological advances and emerging research directions.We anticipate that this Special Issue will foster deeper integration between plant biology and AI,providing a platform to accelerate scientific discovery and contribute to food security and sustainable agriculture.
