POLLEN73S: An image dataset for pollen grains classification

被引：32

作者：

Astolfi, Gilberto ^{[1
,2
]}

Goncalves, Ariadne Barbosa ^{[3
,4
]}

Menezes, Geazy Vilharva ^{[1
]}

Brito Borges, Felipe Silveira ^{[6
]}

Melo Nunes Astolfi, Angelica Christina ^{[5
]}

Matsubara, Edson Takashi ^{[1
]}

Alvarez, Marco ^{[7
]}

Pistori, Hemerson ^{[1
,6
]}

机构：

[1] Univ Fed Mato Grosso do Sul, Coll Comp, Campo Grande, MS, Brazil

[2] Sci & Technol Mato Grosso do Sul, Fed Inst Educ, Campo Grande, MS, Brazil

[3] Univ Fed Mato Grosso do Sul, Postgrad Dept Nat Resources, Campo Grande, MS, Brazil

[4] Univ Estadual Mato Grosso do Sul, Dept Environm Engn, Maracaju, MS, Brazil

[5] Univ Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, Campo Grande, MS, Brazil

[6] Univ Catolica Dom Bosco, Campo Grande, MS, Brazil

[7] Univ Rhode Isl, Dept Comp Sci & Stat, Kingston, RI 02881 USA

来源：

ECOLOGICAL INFORMATICS | 2020年 / 60卷

关键词：

Pollen classification; Convolutional neural networks; Image dataset;

D O I：

10.1016/j.ecoinf.2020.101165

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

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.

引用

页数：9

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