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

Article type: Research Article

Authors: Canul-Chin, Miguel Angel^a | Moguel-Ordóñez, Yolanda Beatriz^b | Martin-Gonzalez, Anabel^{a; *} | Brito-Loeza, Carlos^a | Legarda-Saenz, Ricardo^a

Affiliations: [a] Computational Learning and Imaging Research, Facultad de Matemáticas, Universidad Autónoma deYucatán, Anillo Periférico Norte, Tab. Cat. 13615, Merida, Mexico | [b] Campo Experimental Mocochá-INIFAP, Antiguacarretera Mérida-Motul Km. 24.5, Mococha, Mexico

Correspondence: [*] Corresponding author. Anabel Martin-Gonzalez, Computational Learning and Imaging Research, Facultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tab. Cat. 13615, Merida, Mexico. E-mail: [email protected].

Abstract: 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.

Keywords: Pollen analysis, object detection, palynology, deep learning

DOI: 10.3233/JIFS-219379

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024