Stomatal biology meets multimodal foundation model-driven AI.

Stomata regulate plant growth and environmental responses through their control of gas exchange and transpiration. However, high-throughput stomatal phenotyping and the integration of phenotypic, genetic, and multiomics data remain major challenges for biological discovery and for understanding stomatal function in plants. Here, we review emerging imaging technologies and foundation models for stomatal biology and discuss how advances in AI could transform plant stomatal research. The rapid accumulation of large-scale phenotypic, genetic, and omics datasets requires systematic integration of heterogeneous data and their interpretation to uncover mechanisms of stomatal regulation within a unified reasoning framework. Therefore, we propose a stomatal phenotyping and analysis agent framework that connects these pipelines to accelerate hypothesis generation, phenotype-genotype analysis, discovery of biological function, and crop improvement.