A Stomata Classification and Detection System in Microscope Images of Maize Cultivars
by Aono A. H., Nagai J. S., Dickel G. S., Marinho R. C., De Oliveira P. E. A. M., Faria F. A. (2019)
Alexandre H. Aono a, James S. Nagai a, Gabriella da S. M. Dickel b, Rafaela C. Marinho b, Paulo E. A. M. de Oliveira b, Fabio A. Faria a,∗
a Instituto de Ciéncia e Tecnologia, Universidade Federal de Sao Paulo – UNIFESP 12247-014, S˜ao José dos Campos, SP – Brazil
b Instituto de Biologia, Universidade Federal de Uberlandia
Uberlandia, MG, Brazil
In biorxiv – doi: http://dx.doi.org/10.1101/538165 –
Stomata are morphological structures of plants that have been receiving constant attention. These pores are responsible for the interaction between the internal plant system and the environment, working on different processes such as photosynthesis process and transpiration
As evaluated before, understanding the pore mechanism play a key role to explore the evolution and behavior of plants. Although the study of stomata in dicots species of plants have advanced, there is little information about stomata of cereal grasses. In addition, automated detection of these structures have been presented on the literature, but some gaps are still uncovered.
This fact is motivated by high morphological variation of stomata and the presence of noise from the image acquisition step.
Herein, we propose a new methodology of an automatic stomata classification and detection system in microscope images for maize cultivars. In our experiments, we have achieved an approximated accuracy of 97.1% in the identification of stomata regions using classifiers based on deep learning features.