A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Abstract

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.