Pollen grain recognition using convolutional neural network

This paper addresses two problems: the automated pollen species recognition and counting them on an image obtained with a lighting microscope. Automation of pollen recognition is required in several domains, including allergy and asthma prevention in medicine and honey quality control in the nutrition industry. We propose a deep learning solution based on a convolutional neural network for classification, feature extraction and image segmentation. Our approach achieves state-of-theart results in terms of accuracy. For 5 species, the approach provides 99.8% of accuracy, for 11 species — 95.9%.

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