IMPORTANT: advanced pollen classification of Indian medicinal plants through SEM and computer vision

Abstract

Pollen grains of plant species have unique morphological characteristics. The variability in shape, size, and microscopic pollen surface features can be efficiently used to determine the plant species to which they belong. This approach can be instrumental in regions with rich biodiversity in plant species, specifically in medicinal plant. The creation of a pollen dataset for these species using SEM images and a computer vision application can be beneficial for their identification. We have developed a robust approach utilizing scanning electron microscopy (SEM) to generate high-resolution pollen images from 28 plant species of medicinal importance and employed computer vision techniques for accurate segmentation and classification based on diverse morphological features. In this study, a dataset comprising 269 images for segmentation and 5842 images was used for classification across 28 classes. In addition, we have created a globally accessible database named IMPORTANT (Indian Medicinal Plants Pollen Images Dataset for Research, Training, and Analysis of Neural Networks) to facilitate easy retrieval and sharing of pollen images. This study can effectively identify medicinal plant species by utilizing the microscopic features of pollen SEM images through the IMPORTANT database. Since the pollen of even closely related species vary in their morphological characteristics, this approach can efficiently determine the closely related species with accuracy and precision.