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该研究将有助于开发基于气孔图像的用户界面，即使没有专业的分类知识也能识别物种，并且在药理学、保护生物学、林业和环境科学等领域尤其有用。
O estudo ajudaria a desenvolver uma interface de usuário baseada em imagens estomáticas para identificar espécies mesmo sem conhecimento taxonômico especializado e poderia ser particularmente útil em campos como farmacologia, biologia da conservação, silvicultura e ciências ambientais.
El estudio ayudaría a desarrollar una interfaz de usuario basada en imágenes estomáticas para identificar especies incluso sin conocimientos taxonómicos expertos y podría ser particularmente útil en campos como la farmacología, la biología de la conservación, la silvicultura y la ciencia ambiental.
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Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh
Dey B., Ahmed R., Ferdous J., Haque M. M. U., Khatun R., Hasan F. E., Uddin S. N. (2023)
Biplob Dey, Romel Ahmed, Jannatul Ferdous, Mohammed Masum Ul Haque, Rahela Khatun, Faria Erfana Hasan, Sarder Nasir Uddin,
Shahjalal University of Science and Technology
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Ecological Informatics 75: 102128 – DOI: 10.1016/j.ecoinf.2023.102128 –
https://www.researchgate.net/publication/370766731
Abstract and figures
Stomatal traits of leaves are critical for regulating the exchange of gases between plant tissues and the atmosphere, and thus play a crucial role in the physiological activities of plants. The hypothesis of this study is that distinct stomatal features among different species grown in diverse habitats can serve as a potential marker for species identification. Leaf samples were collected from the mangrove forests of Sundarbans and the freshwater swamp forests of Ratargul in Bangladesh. In total, we examined 11 species from eight different families. We used deep convolutional neural network (DCNN) to automatically identify tree species from microscopic stomatal imprints, as there is currently no established protocol for this task. For model training, 80% (866 images) of the data was used for training the models. Our study observed significant variations in stomatal attributes such as length, width, and density among different species, families, and habitats. These variations could help in accurate species identification by machine learning approaches used in the present study. An empirical comparison was conducted among EfficientNetV2, Xception, VGG16, VGG19, MobileNetV2, ResNet50V2, Resnet152, DenseNet201, and NasNetLarge. We propose a novel approach called the “Normalized Leverage Factor” that utilizes accuracy, precision, recall, and f1-score to select the optimal model. This approach eliminates the non-uniformity of the scores. Although MobileNetV2 achieved an accuracy of 99.06%, our findings indicate that EfficientNetV2 is the optimal model for species identification. This is due to its higher normalized leverage factor (1.92) compared to MobileNetV2 (1.88). The findings demonstrate that plants of diverse habitats show a unique footprint of stomata that offers an innovative method of species identification using DCNN. The study would help to develop a stomatal image-based user interface to identify species even without expert taxonomic knowledge and could be particularly useful in fields such as pharmacology, conservation biology, forestry, and environmental science.
PLANT STOMATA ENCYCLOPEDIA 
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