The CO2 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, 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.
Authors: Miguel Carrrasco 1*, Patricio A. Toledo, Ramiro Velázquez and Odemir M. Bruno *corresponding Author mlacarrasco@gmail.com
Usage: Run: stomata_segmentation.m
This package contains programs written by Carrasco, M.; Toledo, P.A.; Velázquez, R.; Bruno, O.M. for the implementation of Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance described in the article:
Carrasco, M.; Toledo, P.A.; Velázquez, R.; Bruno, O.M. Automatic Stomatal Segmentation Based on
Delaunay-Rayleigh Frequency Distance. Plants 2020, 9, 1613. https://doi.org/10.3390/plants9111613