Detection of bruises on red apples using deep learning models

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dc.authoridUNAL, Zeynep/0000-0002-9954-1151
dc.contributor.authorUnal, Zeynep
dc.contributor.authorKizildeniz, Tefide
dc.contributor.authorOzden, Mustafa
dc.contributor.authorAktas, Hakan
dc.contributor.authorKaragoz, Omer
dc.date.accessioned2024-11-07T13:24:54Z
dc.date.available2024-11-07T13:24:54Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe detection and sorting of bruised apples after harvest play a crucial role in improving their economic value by eliminating surface defects. This also reduces the risk of contamination of infected apples during transport and storage. It can be done by using manual detection or machine vision techniques in red, green, and blue (RGB) colors to detect bruises on apples of various skin colors; however, in the early stages of bruising, it is challenging. Therefore, the main purpose of this study is determin of the effectiveness of Deep Learning models combined with the Near Infrared (NIR) imaging system for naturally bruised Super Chief red apples immediately after harvest. In total, 1000 images for the healthy class and 500 images for the bruised class were acquired from 500 apples. After the images were acquired with the RGB and NIR cameras, the data sets were divided into training (70 %), validation (15 %), and testing (15 %) sets. The Alexnet, the Inceptipon-V3, and the VGG16 network structures were trained using the training and validation data sets, and the trained network was evaluated using the test dataset. The VGG16 model achieved the highest test accuracy (86 %) when trained on the RGB data set, while the AlexNet model exhibited the lowest test accuracy (74.6 %). When the models were trained and tested with NIR datasets, 99.33 %, 100 % and 100 % accuracy rates were obtained for AlexNet, Inception V3, and VGG16, respectively. During the experiments, the VGG16 model trained with the NIR dataset achieved the lowest loss rate of 0.0002, whereas when trained and tested with the RGB dataset, the same VGG16 model also recorded the lowest loss rate of 0.353.These findings indicate that the deep learning models, particularly when trained with NIR data, demonstrate high accuracy rates in classifying apples as healthy or bruised, making them suitable for industrial classification applications. Therefore, the NIR data set is recommended for precise and reliable apple classification in industrial settings.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Nigde Omer Halisdemir University [TGT 2021/22-BAGEP]
dc.description.sponsorshipThe authors acknowledge the support from the Scientific Research Projects Coordination Unit of Nigde Omer Halisdemir University (Project number TGT 2021/22-BAGEP) .
dc.identifier.doi10.1016/j.scienta.2024.113021
dc.identifier.issn0304-4238
dc.identifier.issn1879-1018
dc.identifier.scopus2-s2.0-85186460577
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.scienta.2024.113021
dc.identifier.urihttps://hdl.handle.net/11480/14360
dc.identifier.volume329
dc.identifier.wosWOS:001198975700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofScientia Horticulturae
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectDeep learning
dc.subjectBruise detection
dc.subjectApple classification
dc.subjectCNN Architectures
dc.subjectImage Processing
dc.titleDetection of bruises on red apples using deep learning models
dc.typeArticle

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