Detection of Corn Diseases Using Transformer-Based Deep Learning Models

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dc.contributor.authorKiziloluk, Soner
dc.date.accessioned2026-06-19T06:31:56Z
dc.date.available2026-06-19T06:31:56Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractAs a grain crop, corn is one of the most important products grown worldwide. The timely and accurate identification of diseases on corn leaves is essential for highquality yield and sustainable agricultural productivity. At this point, the application of deep learning models for corn leaf disease detection comes into play. These models provide a quick solution to this problem. Traditional deep learning models, such as Convolutional Neural Networks (CNN), typically operate on local spatial features and within a limited range. Long-range dependencies required for more complex recognition patterns are often overlooked, making these models less effective. Transformer-based models are more adept at capturing global context thanks to the self-attention mechanisms that are part of their architectures. In this study, four different transformer models and two different CNN models were used for corn leaf disease detection. According to the experimental results, MaxViT outperformed other transformer-based models and CNN models, achieving an accuracy of 97.85% and an F1 score of 0.9725. © 2025 IEEE.
dc.identifier.doi10.1109/IDAP68205.2025.11222218
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105025004529
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222218
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4882
dc.indekslendigikaynakScopus
dc.institutionauthorKiziloluk, Soner
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260612
dc.subjectCorn Leaf Diseases
dc.subjectDeep Learning
dc.subjectImage Classification
dc.subjectMaxvit
dc.subjectTransformer-Based Models
dc.titleDetection of Corn Diseases Using Transformer-Based Deep Learning Models
dc.title.alternativeTransformer Tabanli Derin Öǧrenme Modelleri ile Misir Hastaliklarinin Tespiti
dc.typeConference Object

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