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Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms

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Abstract

In order to increase the market value and quality of wheat, it is important to separate different types and determine the amount of foreign matter using the visual properties of durum and bread wheat. In this study, the extreme learning machine (ELM) algorithm, which is often preferred in real-time applications, was used to make classifications using features obtained from images containing the wheat kernel and foreign matter. The feature selection process was applied to remove the irrelevant ones from the obtained 236 features. In addition, the Harris hawks’ optimizer (HHO), a novel method in the literature, and the particle swarm optimizer (PSO), one of the well-known algorithms, were used to improve the ELM model. As part of this study, new models called HHO-ELM and PSO-ELM were created and compared with the original ELM model and other artificial neural networks (ANNs) studies published in the literature. As a result, in comparison with other models, the optimized ELM models demonstrated good stability and accuracy, having 99.32% in binary classification and 95.95% in multi-class classification.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Technology, Selcuk University, 42031, Konya, Turkey

    Musa Dogan & Ilker Ali Ozkan

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  1. Musa Dogan
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MD contributed to conceptualization, methodology, software, validation, formal analysis, visualization, writing—original draft, writing review and editing. IAO contributed to methodology, formal analysis, investigation, writing—original draft, writing—review and editing, supervision, project administration.

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Correspondence to Ilker Ali Ozkan.

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This research is based in part on the Musa Dogan's master’s thesis under direction of the Ilker Ali Ozkan.

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Dogan, M., Ozkan, I.A. Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms. Neural Comput & Applic 35, 12565–12581 (2023). https://doi.org/10.1007/s00521-023-08354-x

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