Characterizing Stomatal and Epidermal Traits Using Peels, Clearing, and AI-Based Image Analysis

Accurate characterization of stomatal morphology and surrounding epidermal traits is essential for understanding stomatal function. A comparative protocol is presented using two complementary techniques-epidermal peels and leaf clearing-to assess stomatal and epidermal cell size, shape, and density in two ecologically distinct dicot species, Fraxinus excelsior and Taraxacum officinale. The protocol includes detailed steps for sample preparation, imaging, and trait quantification using cost- and labor-effective manual measurements (tissue clearing and peeling), and AI-assisted image analysis with the potential to facilitate high throughput applications. Three segmentation tools-a YOLOv8-based stomatal detection model, a generalist deep learning tool for cell segmentation, and a pixel-based classifier-were evaluated for their accuracy and suitability across methods. Significant differences in stomatal and epidermal traits are consistently observed between peeling and clearing techniques in both manual and AI analyses. AI tools enabled reproducible quantification, with YOLOv8-based stomatal detection model (StoManager1) performing best for stomatal traits and a generalist deep learning tool for cell segmentation (Cellpose) for epidermal segmentation. AI-based trait detection was highly consistent in T. officinale but limited in F. excelsior, results still diverged from manual measurements, illustrating how species-specific anatomy and image quality impact method suitability. These findings suggest that while AI-assisted analysis provides a useful framework for stomatal and epidermal trait quantification, expert validation and pilot testing remain essential before broader or high-throughput application.