Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis

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dc.authoridTandirovic Gursel, Amira/0000-0002-9219-3203
dc.contributor.authorCetinkaya, Sueleyman
dc.contributor.authorGursel, Amira Tandirovic
dc.date.accessioned2026-02-27T07:32:50Z
dc.date.available2026-02-27T07:32:50Z
dc.date.issued2025
dc.description.abstractThe health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available.
dc.identifier.doi10.3390/app15158690
dc.identifier.issn2076-3417
dc.identifier.issue15
dc.identifier.urihttp://dx.doi.org/10.3390/app15158690
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4359
dc.identifier.volume15
dc.identifier.wosWOS:001548975500001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectpepper diseases
dc.subjectdiagnosis
dc.subjectcolour enhancement
dc.subjectdeep learning
dc.subjectpre-trained VGG16 model
dc.titlePep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
dc.typeArticle

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