Detection of Corn Diseases Using Transformer-Based Deep Learning Models
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As 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.
Açıklama
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
Anahtar Kelimeler
Corn Leaf Diseases, Deep Learning, Image Classification, Maxvit, Transformer-Based Models
Kaynak
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
WoS Q Değeri
Scopus Q Değeri
N/A
