Artificial intelligence-based classification of pollen grains using attention-guided pollen features aggregation network

Visual classification of pollen grains is crucial for various agricultural applications, particularly for the protection, monitoring, and tracking of flora to preserve the biome and maintain the quality of honey-based products. Traditionally, pollen grain classification has been performed by trained palynologists using a light microscope. Despite their wide range of applications, still tiresome and time-consuming methods are used. Artificial intelligence (AI) can be used to automate the pollen grain classification process. Recently, numerous AI-based techniques for classifying pollen grains have been proposed. However, there is still possibility for performance enhancement including processing time, memory size, and accuracy. In this study, an attention-guided pollen feature aggregation network (APFA-Net) based on deep feature aggregation and channel-wise attention is proposed. Three publicly available datasets, POLLEN73S, POLLEN23E, and Cretan pollen, having a total of 7362 images from 116 distinct pollen types are used for experiments. The proposed method shows F-measure values of 97.37 %, 97.66 %, and 98.39 % with POLLEN73S, POLLEN23E, and Cretan Pollen datasets, respectively. We confirm that our method outperforms existing state-of-the-art methods.