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

Abstract

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.

Plant phenotyping relevance

葉画像から気孔開度を自動抽出する画像解析パイプラインと携帯型撮像装置を開発・性能評価しており、植物表現型取得が中心である。

Code and data availability

The paper's Data Availability section explicitly deposits the authors' ONNX model weights and pipeline code, plus masked/unmasked test images, on a public GitHub repository and Zenodo (DOI 10.5281/zenodo.7549843). These are paper-specific, publicly actionable assets for the stomatal aperture phenotyping pipeline. The Y