Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study

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dc.authoridUNAL, Zeynep/0000-0002-9954-1151
dc.authoridAktas, Hakan/0000-0002-0188-7075
dc.authoridMir, Mohammad Shuaib/0009-0008-3212-5111
dc.authoridGulzar, Yonis/0000-0002-6515-1569
dc.contributor.authorGulzar, Yonis
dc.contributor.authorUnal, Zeynep
dc.contributor.authorAktas, Hakan
dc.contributor.authorMir, Mohammad Shuaib
dc.date.accessioned2024-11-07T13:31:42Z
dc.date.available2024-11-07T13:31:42Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSunflower is an important crop that is susceptible to various diseases, which can significantly impact crop yield and quality. Early and accurate detection of these diseases is crucial for implementing appropriate management strategies. In recent years, deep learning techniques have shown promising results in the field of disease classification using image data. This study presents a comparative analysis of different deep-learning models for the classification of sunflower diseases. five widely used deep learning models, namely AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNet were trained and evaluated using a dataset of sunflower disease images. The performance of each model was measured in terms of precision, recall, F1-score, and accuracy. The experimental results demonstrated that all the deep learning models achieved high precision, recall, F1-score, and accuracy values for sunflower disease classification. Among the models, EfficientNetB3 exhibited the highest precision, recall, F1-score, and accuracy of 0.979. whereas the other models, ALexNet, VGG16, InceptionV3 and MobileNetV3 achieved 0.865, 0.965, 0.954 and 0.969 accuracy respectively. Based on the comparative analysis, it can be concluded that deep learning models are effective for the classification of sunflower diseases. The results highlight the potential of deep learning in early disease detection and classification, which can assist farmers and agronomists in implementing timely disease management strategies. Furthermore, the findings suggest that models like MobileNetV3 and EfficientNetB3 could be preferred choices due to their high performance and relatively fewer training epochs.
dc.description.sponsorshipVice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [625]
dc.description.sponsorshipThis work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, under the Project GRANT 3, 625.
dc.identifier.doi10.3390/agriculture13081479
dc.identifier.issn2077-0472
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85168665830
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/agriculture13081479
dc.identifier.urihttps://hdl.handle.net/11480/14999
dc.identifier.volume13
dc.identifier.wosWOS:001057309800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAgriculture-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectdisease classification
dc.subjectsunflower diseases
dc.subjectartificial intelligence
dc.subjectconvolutional neural networks
dc.subjecttransfer learning
dc.subjectprecision agriculture
dc.subjectadjustable learning
dc.titleHarnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study
dc.typeArticle

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