Image-Based Quantification of Arabidopsis thaliana Stomatal Aperture from Leaf Images

The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from bright-field microscopy images containing mesophyll tissue as noisy backgrounds. By combining a You Only Look Once X-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved a mean average precision with an intersection of union (IoU) threshold of 50% value of 0.875 (stomata detection performance) and an IoU of 0.745 (pore segmentation performance) against images of leaf discs taken with a bright-field microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We demonstrated that this device in combination with fine-tuned models of the pipeline we generated here provides robust measurements that can substitute for manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.

Automated summary

This paper proposes an image analysis pipeline that can automatically quantify the stomatal aperture of Arabidopsis thaliana leaves. By combining a YOLO X-based stomatal detection submodule and a U-Net-based pore segmentation submodule, high-precision detection and segmentation of stomata and pores are achieved. In addition, a portable imaging device is designed for easy acquisition of stomatal images on-site. Utilizing this device and the optimized models, it is possible to replace manual measurement of stomatal response to pathogen inoculation, accelerating research on stomatal biology of model dicots.