Pollen grains detection of four plant species of Yucatan using deep learning

Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin....

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2024-03, p.1-8
Main Authors: Canul-Chin, Miguel Angel, Moguel-Ordóñez, Yolanda Beatriz, Martin-Gonzalez, Anabel, Brito-Loeza, Carlos, Legarda-Saenz, Ricardo
Format: Article
Language:English
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Summary:Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin. This study is a time-consuming process that must be carried out by highly trained palynologists. In this work, we propose an improved model based on a fully convolutional neural network for the automatic detection of pollen grains in microscopic images of four plant species of Yucatan to contribute to the analysis of the honey designation of origin.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-219379