A stomata classification and detection system in microscope images of maize cultivars

Author(s)

Aono, Alexandre H. ; Nagai, James S. ; Dickel, Gabriella da S. M. ; Marinho, Rafaela C. ; de Oliveira, Paulo E. A. M. ; Papa, Joao P. ; Faria, Fabio A.

• Document type: Journal article
• Source: PLoS One; v. 16, n. 10, p. 17-pg., 2021-10-25.

Abstract

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses. (AU)

• FAPESP's process: 18/23908-1 - Towards the Robustness in Deep Learning Architectures for e-Science Applications
• Grantee: Fabio Augusto Faria
• Support Opportunities: Scholarships abroad - Research