Premise: Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Methods and results: We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods.
Conclusions: CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.
Keywords: cell shape; high‐throughput phenotyping; image processing; image segmentation; leaf area index; leaf epidermis; paleobotany; paleoecology.
© 2024 The Authors. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.