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https://hdl.handle.net/20.500.13091/2913
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Büyükarıkan, Birkan | - |
dc.contributor.author | Ülker, Erkan | - |
dc.date.accessioned | 2022-10-08T20:48:57Z | - |
dc.date.available | 2022-10-08T20:48:57Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 0941-0643 | - |
dc.identifier.issn | 1433-3058 | - |
dc.identifier.uri | https://doi.org/10.1007/s00521-022-07350-x | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/2913 | - |
dc.description.abstract | Physiological disorders in apples are due to post-harvest conditions. For this reason, automatic identification of physiological disorders is important in obtaining agricultural information. Image processing is one of the techniques that can help achieve the features of physiological disorders. Physiological disorders during image acquisition can be affected by the changes in brightness values created by different lighting conditions. This changes the results of the classification. In recent years, the convolutional neural network (CNN) has been a successful approach in automatically obtaining deep features from raw images in image classification problems. The study aims to classify physiological disorders using machine learning (ML) methods according to extracted deep features of the images under different lighting conditions. The data sets were created by acquired images (1080 images) and augmentation images (4320 images). Deep features were extracted using five popular pre-trained CNN models in these data sets, and these features were classified using five ML methods. The highest average accuracy was obtained with the VGG19(fc6) + SVM method in the data set-1 and data set-2 and were 96.11 and 96.09%, respectively. With this study, physiological disorders can be determined early, and needed precautions can be taken before and after harvest, not too late. | en_US |
dc.description.sponsorship | Scientific Research Project at Konya Technical University, Konya, Turkey [201113006] | en_US |
dc.description.sponsorship | This work was supported by the Scientific Research Project at Konya Technical University, Konya, Turkey (No. 201113006). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer London Ltd | en_US |
dc.relation.ispartof | Neural Computing & Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Deep features | en_US |
dc.subject | Physiological disorders in apple | en_US |
dc.subject | Lighting | en_US |
dc.subject | Controlled-Atmosphere Storage | en_US |
dc.subject | Near-Infrared Spectroscopy | en_US |
dc.subject | Bitter Pit | en_US |
dc.subject | Defect Detection | en_US |
dc.subject | Braeburn Apple | en_US |
dc.subject | Recognition | en_US |
dc.subject | Reflectance | en_US |
dc.subject | Inspection | en_US |
dc.subject | Products | en_US |
dc.subject | Features | en_US |
dc.title | Classification of physiological disorders in apples fruit using a hybrid model based on convolutional neural network and machine learning methods | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s00521-022-07350-x | - |
dc.identifier.scopus | 2-s2.0-85130717088 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.authorid | Buyukarikan, Birkan/0000-0002-9703-9678 | - |
dc.authorwosid | Buyukarikan, Birkan/F-4244-2019 | - |
dc.identifier.wos | WOS:000802095000003 | en_US |
dc.institutionauthor | Ülker, Erkan | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57710447700 | - |
dc.authorscopusid | 23393979800 | - |
dc.identifier.scopusquality | Q1 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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s00521-022-07350-x.pdf Until 2030-01-01 | 1.6 MB | Adobe PDF | View/Open Request a copy |
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