Plant species identification from pollen grain images using fusion of advanced deep neural networks

Accurate pollen grain classification is essential to advance ecological research, agricultural monitoring, and public health strategies. However, conventional classification methods are degraded in their performance due to their subjective nature, intensive labor, and high variability in the morphology of pollen grains. This study denotes and addresses these critical issues through an extensive study using advanced deep neural networks. We first introduce a new pollen-grain dataset consisting of 52 species of plants. Although the proposed pollen classifications, such as three state-of-the-art deep learning architectures: YOLOv11, Vision Transformer (ViT), and MobileNet achieve impressive performances, their performances are degraded by the denoted issues. Therefore, the augmentation and late-fusion schema are proposed to handle these issues. Through a comprehensive comparative analysis, we demonstrate the performance of the fusion model, combining ViT and YOLOv11 to achieve greater precision. This study facilitates the reliable inference of floral origins from microscopic imagery.