The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology, and melissopalynology. This paper presents a new public annotated image dataset for the Brazilian Savanna called POLLEN73S composed of 2523 images from 73 pollen types. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide a baseline for pollen grain classification. Our experiments showed evidence that DenseNet-201 and ResNet-50 have superior performance against the other CNNs tested, achieving precision results of 95.7% and 94.0%, respectively. Due to its category coverage and satisfactory diversity of examples, POLLEN73S offers a diversity of pollen grain to guide progress in computer vision to solve Palynology problems.

花粉种类和类型的分类是许多领域的重要任务，例如法医古生物学，考古学古生物学和褪黑粉病。本文介绍了巴西大草原的一个新的公开注释的图像数据集，称为POLLEN73S，它由来自73个花粉类型的2523张图像组成。使用最新的卷积神经网络（CNN），我们为花粉粒分类提供了基线。我们的实验表明，DenseNet-201和ResNet-50的性能优于其他测试的CNN，其精确度分别达到95.7％和94.0％。由于其类别覆盖范围和令人满意的示例多样性，POLLEN73S提供了多种花粉粒，可指导计算机视觉技术进步以解决孢粉学问题。