POLLEN73S: An image dataset for pollen grains classification
Date of Original Version
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.
Astolfi, Gilberto, Ariadne B. Gonçalves, Geazy V. Menezes, Felipe S. Borges, Angelica C. Astolfi, Edson T. Matsubara, Marco Alvarez, and Hemerson Pistori. "POLLEN73S: An image dataset for pollen grains classification." Ecological Informatics 60, (2020). doi:10.1016/j.ecoinf.2020.101165.