The interference of optical zoom in human and machine classification of pollen grain images

Abstract

Palynology can be applied to different areas, such as archeology and allergy, where it is constantly growing. However, no publication comparing human classifications with machine learning classifications at different optical scales has been found in the literature. An image dataset with 17 pollen species that occur in Brazil was created, and machine learning algorithms were used for their automatic classification and subsequent comparison with humans. The experiments presented here show how machine and human classification behave according to different optical image scales. Satisfactory results were achieved, with 98.88% average accuracy for the machine and 45.72% for human classification. The results impact a single scale pattern for capturing pollen grain images for both future computer vision experiments and for a faster advance in palynology science.