Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation.

Abstract

Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment. While microscopy is widely used for this purpose, manual analysis is labor-intensive and time-consuming that limit large scale studies. To overcome this, we introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement. Our approach provides a comprehensive analysis of stomatal morphology by examining both stomatal pores and guard cells. A key finding is the introduction of stomatal angles as a novel phenotyping trait, which can offer deeper insights into stomatal function. We developed a model using a carefully annotated dataset that accurately segments and analyzes stomatal guard cells from high-resolution images. Additionally, our study introduces a new opening ratio metric, calculated from the areas of the guard cells and the stomatal pore, providing a valuable morphological descriptor for future physiological research. This scalable system significantly enhances the precision and efficiency of large-scale plant phenotyping, offering a new tool to advance research in plant physiology.

Plant phenotyping relevance

深層学習による気孔画像解析と、気孔形態・角度・開口率の自動推定手法を開発しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper's stomatal image dataset, annotations, and YOLOv8 model are not publicly deposited; the Data availability statement only offers data on reasonable request to the corresponding author. All URLs in the article are generic libraries or cited prior work, not paper-specific assets.

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