Enhanced potato pest identification: a deep learning approach for identifying potato pests

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dc.contributor.authorSohel, Amir
dc.contributor.authorShakil, Md. Shahriar
dc.contributor.authorSiddiquee, Shah Md. Tanvir
dc.contributor.authorMarouf, Ahmed Al
dc.contributor.authorRokne, Jon G.
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2025-11-06T11:31:50Z
dc.date.available2025-11-06T11:31:50Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractPotato crops and their salability are influenced by potato pests in that both crop yield and quality are reduced. This in turn reduces the income for potato farmers due to lower prices for the crop, lower crop yield, trade restriction and reduced market access. Agricultural viability over the long run therefore depends on sustainable pest management. In order to efficiently detect potato pests, a dataset was constructed which contains eight prevalent potato species that were taken from several sources. Image pre-processing techniques were employed enhance image quality for compatibility with deep learning models. Among InceptionV3, VGG-16, and MobileNetV2 models, VGG-16 attained the highest accuracy of 94.44%, outperforming others. Inception-V3 achieved 58% accuracy, while MobileNetV2 reached 75%. Pre-processing has a major influence on improving result accuracy, which emphasizes its significance in enhancing model performance, according to an evaluation of its effects. These findings might lead to the development of pest management strategies for potato farming that are more effective. The efficient use of VGG-16 in potato pest identification systems is demonstrated by its excellent performance. Using deep learning models can therefore reduce financial losses and promote sustainable potato production. This study provides an approach for further investigation into the best ways to control pests in potato production, allowing farmers to overcome the obstacles and take advantage of valuable market prospects even in the face of pest threats.
dc.identifier.citationSohel, A., Shakil, M. S., Siddiquee, S. M. T., Marouf, A. A., Rokne, J. G. ve Alhajj, R. (2024). Enhanced potato pest identification: a deep learning approach for identifying potato pests. IEEE Access, 12, 172149-172161. http://dx.doi.org/10.1109/ACCESS.2024.3488730
dc.identifier.doi10.1109/ACCESS.2024.3488730
dc.identifier.endpage172161
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85208385595
dc.identifier.scopusqualityQ1
dc.identifier.startpage172149
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2024.3488730
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13170
dc.identifier.volume12
dc.identifier.wosWOS:001362127900021
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlhajj, Reda
dc.institutionauthorid0000-0001-6657-9738
dc.language.isoen
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution 4.0 International
dc.subjectClassification
dc.subjectDeep Learning
dc.subjectPotato Pest
dc.subjectVGG-16
dc.titleEnhanced potato pest identification: a deep learning approach for identifying potato pests
dc.typeArticle

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