Volume 20, Issue 1 (March 2024)                   IJEEE 2024, 20(1): 94-110 | Back to browse issues page

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Handa P, Jindal B. FirePSOSA: A Hybrid Metaheuristic Approach for Enhanced Segmentation of Maize Leaves. IJEEE 2024; 20 (1) :94-110
URL: http://ijeee.iust.ac.ir/article-1-3090-en.html
FirePSOSA: A Hybrid Metaheuristic Approach for Enhanced Segmentation of Maize Leaves
Priyanka Handa , Balkrishan Jindal
Abstract:   (726 Views)
The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios.  The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory.
Keywords: Maize leaf, Optimization, PSO, SA, Firefly, Segmentation.
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Type of Study: Research Paper | Subject: Image Processing
Received: 2023/10/11 | Revised: 2024/08/31 | Accepted: 2024/03/19
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.