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Dergiler / NATURENGS / 2025 / Volume: 6 Issue: 2

Artificial Intelligence-Aided Diagnosis in Agriculture: Plant Disease Classification with Vision Transformer and CNN Models

Alim Demir(FIRAT ÜNİVERSİTESİ)0009-0000-2213-5406
Fatih Sarı(FIRAT ÜNİVERSİTESİ)0009-0006-3832-1303
Mehmet Arzu(Fırat Üniversitesi)0000-0001-6610-2788
Mahmut Kaya(FIRAT ÜNİVERSİTESİ)0000-0002-7846-1769
Sayfa
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Yayın Tarihi
2025-12-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These findings confirm the high potential of transformer-based architectures for automated disease detection in agriculture.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study comparatively evaluates the performance of Convolutional Neural Networks (CNN), Vision Transformer (ViT), and Swin Transformer architectures for diagnosing plant leaf diseases.
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Temel Bulgular & Veri:The experimental framework was evaluated using model effectiveness, F1 scores, and confusion matrices on the PlantVillage dataset, which contains 55,448 images from 39 different classes.
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Sonuç & Katkı:These findings confirm the high potential of transformer-based architectures for automated disease detection in agriculture.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Deep Learning · Vision Transformer (ViT) · Swin Transformer

Abstract

This study comparatively evaluates the performance of Convolutional Neural Networks (CNN), Vision Transformer (ViT), and Swin Transformer architectures for diagnosing plant leaf diseases. The experimental framework was evaluated using model effectiveness, F1 scores, and confusion matrices on the PlantVillage dataset, which contains 55,448 images from 39 different classes. The results show that the Swin Transformer achieved the highest diagnostic performance with a 100% F1 score, outperforming both CNN and ViT models. These findings confirm the high potential of transformer-based architectures for automated disease detection in agriculture.

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Bu Makaleye Atıf Yap

Demir, A., Sarı, F., Arzu, M., Kaya, M. (2025). Artificial Intelligence-Aided Diagnosis in Agriculture: Plant Disease Classification with Vision Transformer and CNN Models. NATURENGS, Volume: 6 Issue: 2. https://doi.org/10.46572/naturengs.1832476

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Kaynakça (13)

  1. United Nations Department of Economic and Social Affairs, Population Division (2022). World Population Prospects, Summary of Results, UN DESA/POP/2022/TR/NO. 3, New York.
  2. Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N. and Nelson, A. (2019). The global burden of pathogens and pests on major food crops, Nature Ecology & Evolution, 3: 430–439.
  3. James, W.C. (1974). Assessment of plant diseases and losses, Annual Review of Phytopathology, 12: 27–48.
  4. Shoaib, M., Shah, B., El-Sappagh, S., Ali, A., Ullah, A., Alenezi, F. and Gehu, T. (2023). Deep Learning for Plant Disease Detection: A Comprehensive Review, IEEE Access, 11: 87384-87409.
  5. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NeurIPS), Lake Tahoe, v. 25.
  6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I. (2017). Attention is all you need, Advances in Neural Information Processing Systems (NeurIPS), Long Beach, v. 30.
  7. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, International Conference on Learning Representations (ICLR), Vienna.
  8. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 10012–10022.
  9. Beyer, L., Zhai, X. and Kolesnikov, A. (2022). Better plain ViT baselines for ImageNet-1k, arXiv preprint arXiv:2205.01580.
  10. Dehghani, M., Mustafa, B., Djolonga, J., Heek, J., Minderer, M., Caron, M., Steiner, A., Puigcerver, J., Geirhos, R., Alabdulmohsin, I., et al. (2024). Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution, Advances in Neural Information Processing Systems (NeurIPS), New Orleans, v. 36.
  11. Hassani, A., Walton, S., Shah, N., Abuduweili, A., Li, J. and Shi, H. (2021). Escaping the Big Data Paradigm with Compact Transformers, arXiv preprint arXiv:2104.05704.
  12. Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L. and Zhang, L. (2021). CvT: Introducing Convolutions to Vision Transformers, IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 22–31.
  13. Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., Jégou, H. and Douze, M. (2021). LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference, IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 12259–12269.