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Automated pollen classification is challenging because visually similar species may differ only by subtle morphological characteristics. This thesis investigates deep-learning models for classifying microscopic pollen-grain images from ten plant species using RGB image crops together with two segmentation-derived size measurements: the major and minor axes of the pollen grain. The study builds on
