The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI

Basit Öğe Kaydı
dc.contributor.authorAtesoglu, Fatih
dc.contributor.authorBingol, Harun
dc.date.accessioned2026-06-19T06:37:54Z
dc.date.available2026-06-19T06:37:54Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThere are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases.
dc.identifier.doi10.3390/agriengineering7070228
dc.identifier.issn2624-7402
dc.identifier.issue7
dc.identifier.scopus2-s2.0-105011496717
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/agriengineering7070228
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5273
dc.identifier.volume7
dc.identifier.wosWOS:001539544900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAgriengineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectCnn
dc.subjectGrape Leaf Disease
dc.subjectHog
dc.subjectLbp
dc.subjectMachine Learning
dc.subjectNca
dc.titleThe Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu