Research on stomata, i.e., morphological structures of plants, has increased in popularity in the last years. These structures (pores) are in charge of the interaction between the internal plant system and the environment, working on different processes such as photosynthesis and transpiration stream. Besides, a better understanding of the pore mechanism plays a significant role when exploring the evolution process, as well as the behavior of plants. Although the study of stomata in dicots species of plants has advanced considerably in the past years, there is little information about stomata of cereal grasses. Also, automated detection of these structures have been considered in 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. In this work, we propose a new methodology for automatic stomata classification and a new detection system in microscope images for maize cultivars. We have achieved an approximated accuracy of 97.1% in the identification of stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system.

中文翻译：

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玉米品种显微图像气孔分类与检测系统

近年来，对气孔（即植物的形态结构）的研究日益普及。这些结构（孔）负责内部植物系统与环境之间的相互作用，并在光合作用和蒸腾流等不同过程中起作用。此外，在探索进化过程以及植物的行为时，对孔机制的更好理解也起着重要作用。尽管在过去的几年中，植物双子叶植物气孔的研究已经取得了很大进展，但是关于禾谷类植物气孔的信息却很少。同样，在文献中已经考虑了对这些结构的自动检测，但是仍然存在一些空白。这个事实是由气孔的高度形态变化和图像采集步骤中存在噪声引起的。在这项工作中，我们提出了一种用于气孔自动分类的新方法以及一种用于玉米品种的显微镜图像中的新检测系统。使用基于深度学习特征的分类器，在气孔区域识别中，我们已达到大约97.1％的准确度，这被认为是一种近乎完美的分类系统。