A Comparison Study of Deep Convolutional Neural Networks for the Classification of Brazilian Savannah Pollen Grains: Preliminary Results

Abstract

The classification of pollen grains images are currently done manually and visually, being a weariful task and predisposed to mistakes due to human exhaustion. In this paper, the authors introduce an automatic classification of 55 different pollen grain classes, using convolutional neural networks. Different architectures and hyperparameters were used to improve the classification result. Using the networks VGG16, VGG19, and InceptionV3, were obtained accuracy rates over 93.58%.