APPLICATION OF FUZZY METRICS IN CLUSTERING PROBLEMS OF AGRICULTURAL CROP VARIETIES

Andrijana  Stamenković; Nataša  Milosavljević; Nebojša Ralević

doi:10.59267/ekoPolj2401121S

Authors

Andrijana Stamenković University of Novi Sad, Faculty of Technical Science, Department of Power, Electronic and Telecommunication Engineering, Novi Sad, Republic of Serbia https://orcid.org/0009-0003-4452-6370
Nataša Milosavljević University of Belgrade, Faculty of Agriculture, Department of Mathematics and Physics, Zemun, Republic of Serbia https://orcid.org/0000-0003-4056-089X
Nebojša Ralević University of Novi Sad, Faculty of Technical Science, Department of Fundamental Sciences, Novi Sad, Republic of Serbia https://orcid.org/0000-0002-3825-9822

DOI:

https://doi.org/10.59267/ekoPolj2401121S

Keywords:

agricultural crop varieties, fuzzy metrics, mathematical modeling, machine learning, clustering, variable environment method

Abstract

The problem of image-based detection of the variety of beans, using artificial intelligence, is currently dealt with by scientists of various profiles. The idea of this paper is to show the possibility of applying different types of distances, primarily those that are fuzzy metrics, in clustering models in order to improve existing models and obtain more accurate results. The paper presents the method of variable neighborhood search, which uses both standard and fuzzy t-metrics and dual fuzzy s-metrics characterized by appropriate parameters. By varying those parameters of the fuzzy metric as well as the parameters of the metaheuristic used, we have shown how it is possible to improve the clustering results. The obtained results were compared with existing ones from the literature. The criterion function used in clustering is a fuzzy metric, which is proven in the paper.

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