Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture

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dc.authoridYurdakul, Mustafa/0000-0003-0562-4931
dc.contributor.authorYurdakul, Mustafa
dc.contributor.authorAtabas, Irfan
dc.contributor.authorTasdemir, Sakir
dc.date.accessioned2025-01-21T16:35:32Z
dc.date.available2025-01-21T16:35:32Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAlmond (Prunus dulcis) is a nutritious food with a rich content. In addition to consuming as food, it is also used for various purposes in sectors such as medicine, cosmetics and bioenergy. With all these usages, almond has become a globally demanded product. Accurately determining almond variety is crucial for quality assessment and market value. Convolutional Neural Network (CNN) has a great performance in image classification. In this study, a public dataset containing images of four different almond varieties was created. Five well-known and light-weight CNN models (DenseNet121, EfficientNetB0, MobileNet, MobileNet V2, NASNetMobile) were used to classify almond images. Additionally, a model called 'Genetic CNN', which has its hyperparameters determined by Genetic Algorithm, was proposed. Among the well-known and light-weight CNN models, NASNetMobile achieved the most successful result with an accuracy rate of 99.20%, precision of 99.21%, recall of 99.20% and f1-score of 99.19%. Genetic CNN outperformed well-known models with an accuracy rate of 99.55%, precision of 99.56%, recall of 99.55% and f1-score of 99.55%. Furthermore, the Genetic CNN model has a relatively small size and low test time in comparison to other models, with a parameter count of only 1.1 million. Genetic CNN is suitable for embedded and mobile systems and can be used in real-life solutions.
dc.description.sponsorshipKirikkale University
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1007/s00217-024-04562-4
dc.identifier.endpage2638
dc.identifier.issn1438-2377
dc.identifier.issn1438-2385
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85193294490
dc.identifier.scopusqualityQ1
dc.identifier.startpage2625
dc.identifier.urihttps://doi.org/10.1007/s00217-024-04562-4
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24152
dc.identifier.volume250
dc.identifier.wosWOS:001226610600002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEuropean Food Research and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectAlmond classification; Convolutional neural networks; Genetic algorithm; Deep learning; Optimization
dc.titleAlmond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture
dc.typeArticle

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