Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance.

Abstract

The CO 2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.

Plant phenotyping relevance

葉の顕微鏡画像から気孔を自動セグメンテーションする手法の開発が中心であり、植物の形態的形質取得に直接関わる。

Code and data availability

The paper's Supplementary Materials section explicitly states that the DRTB solution (authors' analysis code) is available online at https://github.com/mlacarrasco/drtb and that the images database (stomatal microscopy images used for phenotyping) is available at https://github.com/mlacarrasco/drtb/tree/main/database.