A Hybrid Grey Wolves Optimizer and Convolutional Neural Network for Pollen Grain Recognition

Hanane Menad, Farah Ben-naoum and Abdelmalek Amine
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Hanane Menad: EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria
Farah Ben-naoum: EEDIS Laboratory, University of Djillali Liabes, Sidi Bel Abbes, Algeria
Abdelmalek Amine: GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saida, Algeria

International Journal of Swarm Intelligence Research (IJSIR), 2020, vol. 11, issue 3, 49-71

Abstract: Melissopalynology, or pollen analysis of honey, is one of the areas that benefited greatly from image processing and analysis techniques, where melissopalynology is the science that studies the pollen contained in honey, using a microscopic examination. Nowadays, developing an automatic classification system for pollen identification presents a challenge. This article presents a metaheuristic for image segmentation to detect pollen grains in images. It is a swarm intelligence technique inspired from grey wolf hunting behavior in nature, centered around respecting the hierarchy of a pack. It was tested on a set of microscopic images of pollen grains. To evaluate pollen detection, we represented the detected pollen grains using two methods, grey-level based representations where we kept grey value of each pixel, and a binary mask-based technique, where a pixel could have only two values (1 or 0). Then, we used a convolutional neural network (CNN) technique for image classification to predict the specie of each pollen. The proposed system was tested on a set of microscopic images of pollen grains. The obtained performance measures of the system proved that the system is very successful.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:11:y:2020:i:3:p:49-71

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