Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding

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dc.contributor.authorOrtega Sánchez, Noé
dc.contributor.authorRodríguez Esparza, Erick
dc.contributor.authorOliva, Diego
dc.contributor.authorPérez Cisneros, Marco
dc.contributor.authorMohamed, Ali Wagdy
dc.contributor.authorDhiman, Gaurav
dc.contributor.authorHernández Montelongo, Rosaura
dc.date.accessioned2025-11-03T09:53:13Z
dc.date.available2025-11-03T09:53:13Z
dc.date.issued2022-03
dc.date.updated2025-11-03T09:53:13Z
dc.description.abstractIdentifying the defects in apples is commonly done with visual examination techniques. However, it is a slow and laborious process. Image processing techniques have begun to be used to help and make the diagnosis of fruit diseases more efficient. In image processing systems, the segmentation of regions in the scenes is a crucial step. Specifically for images from apples, disease segmentation is a complicated task due to the different elements that affect the acquisition of the images. In addition, apple diseases also have features that need to be segmented. In this work, an efficient approach that uses the Gaining-sharing Knowledge-based (GSK) algorithm is proposed to optimize the minimum cross-entropy thresholding (MCET) for the segmentation of apple images highlighting the diseases defects. The proposed MCET-GSK has been tested for experimental purposes over different images and compared with various metaheuristics. The experiments were conducted to provide evidence of the GSK’s optimization capabilities by performing the Wilcoxon test and applying a set of metrics to verify the quality of the segmented images. The experimental results validate the performance of the MCET-GSK in the segmentation of apple images by adequately separating the regions with damage produced by a disease. The quality of the segmentation is superior compared with other similar approaches.en
dc.identifier.citationOrtega-Sánchez, N., Rodríguez-Esparza, E., Oliva, D., Pérez-Cisneros, M., Mohamed, A. W., Dhiman, G., & Hernández-Montelongo, R. (2022). Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding. Soft Computing, 26(5), 2587-2623. https://doi.org/10.1007/S00500-021-06418-5
dc.identifier.doi10.1007/S00500-021-06418-5
dc.identifier.eissn1433-7479
dc.identifier.issn1432-7643
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4216
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
dc.subject.otherApple defects
dc.subject.otherApple diseases identification
dc.subject.otherGaining Sharing Knowledge based (GSK)
dc.subject.otherMetaheuristics
dc.subject.otherMultilevel Segmentation
dc.titleIdentification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholdingen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.endPage2623
oaire.citation.issue5
oaire.citation.startPage2587
oaire.citation.titleSoft Computing
oaire.citation.volume26
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