Rapid non-destructive method to phenotype stomatal traits

Abstract

Background: Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis. Results: The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species. Conclusions: We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

StomaVision: stomatal trait analysis through deep learning

“…It is tailored to ensure seamless interaction for users, eliminating the need to differentiate between dicot and monocot settings, specify expected stomatal sizes, or select types of microscope images. (Fetter et al, 2019;Li et al, 2022;Pathoumthong et al, 2023).…”

“…Recent advancements in Deep Neural Networks (DNNs) have significantly improved the performance of classic computer vision tasks for image classification, object detection, semantic segmentation, and instance segmentation in plant phenotype analysis (Miao et al ., 2021; Tu et al ., 2022; Wang et al ., 2022). The use of DNNs to identify stomata has shown improved performance in various tasks (Toda et al ., 2018; Fetter et al ., 2019; Li et al ., 2019, 2022; Sakoda et al ., 2019; Aono et al ., 2021; Jayakody et al ., 2021; Zhu et al ., 2021; Liang et al ., 2022; Pathoumthong et al ., 2023; Sai et al ., 2023). Nevertheless, there are still significant limitations that impede their adoption and application.…”

“…Nevertheless, there are still significant limitations that impede their adoption and application. One major limitation is that most published methods can only detect (count) stomata (Fetter et al ., 2019; Sakoda et al ., 2019; Aono et al ., 2021; Jayakody et al ., 2021; Zhu et al ., 2021) and have a limited ability to quantify critical stomatal parameters, such as aspect ratio, pore size, and dynamic states (open versus closed) (Jayakody et al ., 2017; Toda et al ., 2018; Bheemanahalli et al ., 2021; Liang et al ., 2022; Li et al ., 2022; Pathoumthong et al ., 2023; Sai et al ., 2023; Wang et al ., 2024). This limited functionality restricts the depth of physiological insights gained from these analyses.…”

Natural variation in the pattern‐triggered immunity response in plants: Investigations, implications and applications

“…When measuring stomatal aperture size, it is important to quickly fix the stomata at the time point desired, which can be achieved by several methods (Wu & Zhao, 2017). Stomatal closure has also been measured in live plants using handheld microscopes and indirectly via its effect on leaf temperature using thermal imaging, both of which allow higher‐throughput phenotyping than traditional microscopy (Ceulemans et al., 1995; Merlot et al., 2002; Pathoumthong et al., 2023). Using this approach to measure PTI would require applying a PAMP directly to the live plant, which may not always be feasible.…”

REGULATOR OF FLOWERING AND STRESS manipulates stomatal density and size in Brachypodium

“…The 10-day water deprivation treatment was conducted as aforementioned. To image the stomatal features on the abaxial surface, leaves were processed with the nail polish method (Pathoumthong et al, 2023). Stomata on leaf imprints were observed using a light microscope (OLYMPUS IXplore compound microscope) with 10Â or 40Â objectives.…”