Pollen Grains Classification with a Deep Learning System GPU-Trained



Convolutional Neural Network, Deep Learning, Graphics Processing Unit, Transfer Learning


Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in many computer vision tasks such as classification, due to this great performance the computational requirements have increased considerably; therefore, it is advisable to use new platforms such as the Graphics Processing Unit (GPU), which offer large computational resources for the development of new systems with CNN. This paper presents the GPU-Trained implementation of a DL system with the CNN algorithm, proposing a CNN model capable of running on a GPU in real-time for the automatic classification of 19 different pollen grains belonging to 14 different families, which are found in high concentrations in Mexico, and which are large interest in areas such as beekeeping, paleoecology, botany, allergology, agriculture among others. These areas seek to improve the collection of palynological data in terms of time and accuracy. In order to evaluate our model, evaluation tests were performed in the NVIDIA Jetson TX2 Developer Kit GPU. Experimental results achieves around 90% in CCR and Sensitivity in the proposed model. Additionally, the proposed model works at a processing speed of 6,826 Frames Per Second (FPS) and has   approximately 50% fewer parameters than reported in related works.


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Author Biographies

Susana Ortega Cisneros, CINVESTAV, Unidad Guadalajara

Susana Ortega Cisneros received the engineering degree in electronics and communications engineering from the University of Guadalajara, Guadalajara, Jalisco, Mexico, in 1990, the M.Sci. degree in solid state electronics from the Center for Research and Advanced Studies (Cinvestav), National Polytechnic Institute, Mexico City, Mexico, in 1995, and the Ph.D. degree in computer science and telecommunications from the Autonomous University of Madrid, Madrid, Spain, in 2005. Since 2010, she has been with the Center for Research and Advanced Studies (Cinvestav), National Polytechnic Institute, Guadalajara, Jalisco, Mexico. She is involved in the design of digital architectures based on FPGAs, digital signal processors (DSPs), and microprocessors. Her current research interests include digital control, self-timed synchronization, electronic systems applied to biomedicine, embedded microprocessor design, digital electronics, custom DSPs in FPGAs, and, recently, remote sensing applications.

Juan Manuel Ruiz Varela, Cinvestav Unidad Guadalajara

Juan Manuel Ruiz Varela received the B.S. degree in Electronics from the Technological Institute of Oaxaca, Mexico, in 2014, the Master’s degree from the Center for Research and Advanced Studies (Cinvestav), National Polytechnic Institute (IPN) Jalisco, Mexico, currently, he is a Ph.D. student in the Center for Research and Advanced Studies, National Polytechnic Institute (IPN) Jalisco, Mexico. He is involved in the design of digital architectures based on FPGAs, his current research interests include electronic systems applied to biomedicine, Computer Vision and Deep Learning.

Miguel Angel Rivera Acosta, Cinvestav Unidad Guadalajara

Miguel Rivera Acosta received the M.S. degree in electrical engineering from Center for Research and Advanced Studies of the National Polytechnic Institute in 2016. Currently, he is working toward the Ph.D. degree at the Center for Research and Advanced Studies of the National Polytechnic Institute in Guadalajara, Mexico. His research interests include microprocessor and ASIC design for digital image processing and computer vision.

Jorge Rivera Dominguez, Cinvestav Unidad Guadalajara

He received the M.S. and Ph.D. degrees in electrical engineering from Center for Research and Advanced Studies (CINVESTAV) of the National Polytechnic Institute (IPN), Guadalajara, Mexico in 2001 and 2005, respectively. He is currently commissioned as Professor of CONACYT in CINVESTAV Guadalajara. His research interests are focused on the design of control algorithms with their corresponding implementation in reconfigurable digital devices.

Pablo Moreno Villalobos, Cinvestav Unidad Guadalajara

Pablo Moreno received the M.S. degree in Electrical Engineering from the Monterrey Institute of Technology and Higher Education (ITESM), Mexico in 1989 and the Ph.D. degree in Electrical Engineering from the Washington State University, United States in 1997. Currently, he is with the Center for Research and Advanced Studies (Cinvestav), National Polytechnic Institute, Jalisco, Mexico. His current research interests include electrophysics and wave propagation.


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How to Cite

Ortega Cisneros, S. ., Ruiz Varela, J. M., Rivera Acosta, M. A., Rivera Dominguez, J., & Moreno Villalobos, P. (2021). Pollen Grains Classification with a Deep Learning System GPU-Trained. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/4685