DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation

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dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorTopal, Ahmet
dc.contributor.authorTunga, Burcu
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-05-09T13:06:39Z
dc.date.available2025-05-09T13:06:39Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractPlant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation(HDMR)to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems. © 2024 Topal et al.
dc.identifier.citationTopal, A., Tunga, B., & Tirkolaee, E. B. (2024). DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation. PeerJ Computer Science, 10, e2406.
dc.identifier.doi10.7717/peerj-cs.2406
dc.identifier.issn23765992
dc.identifier.pmid39650461
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.7717/peerj-cs.2406
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7253
dc.identifier.volume10
dc.identifier.wosWOS:001356227000006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherPeerJ Inc.
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectEnhanced Multivariance Product Representation
dc.subjectHigh Dimensional Model Representation
dc.subjectPlant Disease
dc.titleDeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation
dc.typeArticle

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