Pollen Grain Classification using Geometrical and Textural Features

This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images. Pollen grain classification has received notable attention concerning a wide range of applications such as paleontology and honey certification, forecasting of allergies caused of airborne pollen and food technology. It requires an extensive qualitative process that is mostly performed manually by an expert. Although manual classification shows satisfactory performance, it may suffer from intra and inter-observer variability and it is time consuming. This study benefits from the advances of image processing and machine learning and proposes a fully-automated analysis pipeline aiming to: A) calculate morphological characteristics from the images using a cost-effective microscope, and b) classify images into 6 pollen classes. A private dataset from the Department of Agriculture of the Hellenic Mediterranean University in Crete containing 564 images was used in this study. A Random Forest (RF) classifier was utilized to classify images. A repeated nested cross-validation (nested-CV) schema was used to estimate the generalization performance and prevent overfitting. Image preprocessing, extraction of geometric and textural characteristics and feature selection were implemented prior to the assessment of the classification performance and a mean accuracy of 88.24% was reported.