Carrasco Miguel, Toledo Patricio A, Velázquez Ramiro, Bruno Odemir M
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez, Av. Diagonal Las Torres, 2700 Santiago, Chile.
Facultad de Ingeniería Josemaría Escrivá de Balaguer 101, Campus Aguascalientes, Universidad Panamericana, Aguascalientes 20290, Mexico.
Plants (Basel). 2020 Nov 20;9(11):1613. doi: 10.3390/plants9111613.
The CO 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.
叶片与大气之间的二氧化碳和水汽交换与植物生理学相关。这个过程通过气孔完成。这些结构在植物研究中至关重要，因为它们的特性与植物的进化过程以及环境和植物激素条件相关。由于微观图像的噪声和形态，气孔检测是一项复杂的任务。尽管近年来已经开发出了使这个过程自动化的分割算法，但它们都使用探索颜色特征的技术。本研究探索了植物中的一个独特特征，它对应于叶片结构内气孔的空间分布。与基于深度学习工具的分割技术不同，我们强调寻找最佳阈值水平，以便能够检测到高比例的气孔，而与气孔的大小和形状无关。除了盐形成和其他生物形成中的几何结构形成结果外，这一最后特征在文献中尚未有报道。
