Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection

dc.authorid0000-0002-0381-9631en_US
dc.contributor.authorKızıloluk, Soner
dc.date.accessioned2022-03-22T13:18:42Z
dc.date.available2022-03-22T13:18:42Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractPlant diseases lead to a significant decrease in product efficiency and economic losses for producers. However, early detection of plant diseases plays an important role in preventing these losses. Today, Convolutional Neural Network (CNN) models are widely used for image processing in many fields such as face recognition, climate, health, and agriculture. But in these models, the weights of the layers are randomly initialized during training, which increases training time and decreases performance. With the method known as Transfer Learning in the literature, CNN models are trained on large databases such as ImageNet. Then, pretrained CNN models are created using the weights obtained in this training. Thus, training time decreases while performance improves. In this study, standard and pretrained versions of popular CNN models DarkNet-19, GoogleNet, Inception-v3, Resnet-18, and ShuffleNet have been used for automatic classification of diseases from leaf images of potato, cotton, bean, and banana. In the experimental study, the classification performances of all these standard and pretrained CNN models are presented comparatively. Experimental results have shown that the performance of CNN models is significantly improved by transfer learning, even in a small number of epochsen_US
dc.identifier.citationKIZILOLUK, S. Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection. NATURENGS, 2(2), 86-99.en_US
dc.identifier.doi10.46572/naturengs.1007532
dc.identifier.endpage99en_US
dc.identifier.issue2en_US
dc.identifier.startpage86en_US
dc.identifier.urihttps://doi.org/10.46572/naturengs.1007532
dc.identifier.urihttps://hdl.handle.net/20.500.12899/800
dc.identifier.volume2en_US
dc.language.isoenen_US
dc.publisherMalatya Turgut Ozal Universityen_US
dc.relation.ispartofNATURENGSen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectTransfer Learninen_US
dc.subjectDisease Detectionen_US
dc.subjectClassificationen_US
dc.titleComparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detectionen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
10.46572-naturengs.1007532-2018391.pdf
Boyut:
4.56 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: