Classification of pollen grain images with MobileNet

The analysis of pollen grains is a prominent task in areas such as ecology, food engineering, and others that have different purposes, such as identifying the origin of honey, as well as helping in the development of new products or evaluating the quality of the products. This research presents a CN...

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Bibliographic Details
Published in:	2021 XLVII Latin American Computing Conference (CLEI) pp. 1 - 10
Main Authors:	da Silva Soares, Julio Cesar, Aires, Kelson Romulo Teixeira, dos Santos, Alan Rafael Ferreira, de M. S. Veras, Rodrigo, y, Otilio Paulo, Neto, Gilberto Nunes, de Araujo, Flavio Henrique Duarte
Format:	Conference Proceeding
Language:	English
Published:	IEEE 25-10-2021
Subjects:	Convolutional neural networks Data Augmentation Deep Learning Image color analysis Microscopy Pollen Grains Random access memory Scanning electron microscopy Support vector machines Transfer Learning Visualization
Online Access:	Get full text
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Summary:	The analysis of pollen grains is a prominent task in areas such as ecology, food engineering, and others that have different purposes, such as identifying the origin of honey, as well as helping in the development of new products or evaluating the quality of the products. This research presents a CNN architecture to classify pollen grains that can have performance equal to or superior to those found in the literature. Using POLEN23E database. Two experiments were performed with this database, one of which used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case. Two experiments were performed where one of them used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case.
DOI:	10.1109/CLEI53233.2021.9639998