Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification

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dc.contributor.authorYurdakul, Mustafa
dc.contributor.authorAtabas, Irfan
dc.contributor.authorTasdemir, Sakir
dc.date.accessioned2025-01-21T16:26:37Z
dc.date.available2025-01-21T16:26:37Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024 -- 9 February 2024 through 10 February 2024 -- Bhubaneswar -- 198536
dc.description.abstractAlmond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. The market value of almonds is determined according to the quality of the almonds. Manually determining the quality of almonds by humans is a prone to error, time-consuming, and tiring process. In this study, For this reasons, well-known twelve pre-trained CNNs were used to classify almonds as normal and damaged. Then, the most successful model was used as a feature extractor, and the features were classified with various machine learning algorithms. In addition to all these, features were selected by using the FPA algorithm, and the classification process was carried out. Experimental results showed that the use of CNNs as feature extractors and classification with machine learning algorithms can provide better results than the classical softmax structure. In addition, the proposed FPA-based feature extraction increases the classification performance. © 2024 IEEE.
dc.identifier.doi10.1109/ESIC60604.2024.10481555
dc.identifier.endpage438
dc.identifier.isbn979-835034985-6
dc.identifier.scopus2-s2.0-85191015400
dc.identifier.scopusqualityN/A
dc.identifier.startpage433
dc.identifier.urihttps://doi.org/10.1109/ESIC60604.2024.10481555
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23145
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectAlmond; Artificial Bee Colony; Classification; CNN; Feature Extraction; Flower Pollination Optimization
dc.titleFlower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification
dc.typeConference Object

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