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Pollen Detection in Images Using Genetic Algorithms and Tabu Search

Pollen Detection in Images Using Genetic Algorithms and Tabu Search

Hanane Menad, Farah Ben-naoum, Abdelmalek Amine
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 16
ISSN: 1947-8402|EISSN: 1947-8410|EISBN13: 9781683181989|DOI: 10.4018/IJSESD.287877
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MLA

Menad, Hanane, et al. "Pollen Detection in Images Using Genetic Algorithms and Tabu Search." IJSESD vol.13, no.1 2022: pp.1-16. http://doi.org/10.4018/IJSESD.287877

APA

Menad, H., Farah Ben-naoum, & Amine, A. (2022). Pollen Detection in Images Using Genetic Algorithms and Tabu Search. International Journal of Social Ecology and Sustainable Development (IJSESD), 13(1), 1-16. http://doi.org/10.4018/IJSESD.287877

Chicago

Menad, Hanane, Farah Ben-naoum, and Abdelmalek Amine. "Pollen Detection in Images Using Genetic Algorithms and Tabu Search," International Journal of Social Ecology and Sustainable Development (IJSESD) 13, no.1: 1-16. http://doi.org/10.4018/IJSESD.287877

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Abstract

Pollen recognition is one of the most active research areas in field of ecological modeling. It is done either via microscopic images analysis of pollen grains, or via chemical components analysis. In this paper, we were interested in pollen images analysis, in which we proposed an approach for image segmentation in order to detect pollen grains in the microscopic images. The approach starts by generating two pixels using genetic algorithms where one pixel of the selected ones is a pollen pixel while the other is background pixels, then we used kmeans algorithm for image pixels clustering to segment the input image, after that we classified the segmented images using machine learning technics, and finally, we used taboo search to save the best pixels chosen by genetic algorithm based on the obtained accuracy as fitness function. The obtained results proved the efficiency of the proposed system where it could recognize 96.4% of the pollen grains.

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