SavedAdd your journal

Journals / Türk Tarım ve Doğa Bilimleri Dergisi / 2026 / Volume: 12 Issue: 3

Comparative Performance Analysis of Deep Learning Based CNN Models for Diagnosis of Corn Leaf Diseases

Adnan Gökten(Çukurova Üniversitesi)0000-0001-8988-9720
Erkut Tekeli(ADANA ALPARSLAN TÜRKEŞ BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ)0000-0001-9468-5378
Pages
—
Publication Date
2025-07-23
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that modern architectures (especially ConvNeXtNet) provide higher performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study comparatively analyses the effectiveness of deep learning based on the convolutional neural network (CNN) models in the diagnosis of corn leaf diseases.
📊
Key Findings & Data:Accurate and rapid diagnosis of leaf diseases, which cause serious economic losses in agricultural production, is critical for production efficiency.
💡
Conclusion & Impact:The results showed that modern architectures (especially ConvNeXtNet) provide higher performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:: Deep learning · corn leaf diseases · CNN

Abstract

This study comparatively analyses the effectiveness of deep learning based on the convolutional neural network (CNN) models in the diagnosis of corn leaf diseases. Accurate and rapid diagnosis of leaf diseases, which cause serious economic losses in agricultural production, is critical for production efficiency. In this context, different CNN architectures (AlexNet, GoogLeNet, ResNet, DenseNet, EfficientNet, MobileNet and ConvNeXt) were used to perform classification on Corn leaf images. Model performances were evaluated with metrics such as accuracy, F1 score, precision and ROC-AUC. The results showed that modern architectures (especially ConvNeXtNet) provide higher performance. These findings support the practical applicability of artificial intelligence-supported automatic diagnosis systems in agriculture.

🔖

Cite This Article

Gökten, A., Tekeli, E. (2025). Comparative Performance Analysis of Deep Learning Based CNN Models for Diagnosis of Corn Leaf Diseases. Türk Tarım ve Doğa Bilimleri Dergisi, Volume: 12 Issue: 3. https://doi.org/10.30910/turkjans.1695283

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Bruns, H. A. (2017). Southern corn leaf blight: A story worth retelling. Agronomy Journal, 109(4), 1218-1224. https://doi.org/10.2134/agronj2017.01.0006
  2. Chen, J., Wang, W., Zhang, D., Zeb, A., & Nanehkaran, Y. A. (2021). Attention embedded lightweight network for maize disease recognition. Plant Pathology, 70(3), 630-642. https://doi.org/10.1111/ppa.13322
  3. Ciran, A., & Özbay, E. (2022). Ön-Eğitimli CNN Mimarilerinin Füzyonu ile Mısır Yaprağı Hastalıklarının Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, 44, 74-83. https://doi.org/10.31590/ejosat.1216356
  4. Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., & Prasanna, B. M. (2022). Global maize production, consumption and trade: trends and R&D implications. Food Security, 14(5), 1295-1319. https://doi.org/10.1007/s12571-022-01288-7
  5. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6325-6334.
  6. J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1
  7. Lenk, F., Bröring, S., Herzog, P., & Leker, J. (2007). On the usage of agricultural raw materials - Energy or food? An assessment from an economics perspective. Biotechnology Journal, 2(12), 1497-1504. https://doi.org/10.1002/biot.200700153
  8. Liu, Z., Mao, H., Wu, C. Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A ConvNet for the 2020s. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11976-11986.
  9. Megersa, Z. M., Adege, A. B., & Rashid, F. (2023). Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network. Knowledge, 4(4), 615-634.
  10. Mehta, S., Kukreja, V., & Gupta, A. (2023). Revolutionizing Maize Disease Management with Federated Learning CNNs: A Decentralized and Privacy-Sensitive Approach. 2023 4th International Conference for Emerging Technology, INCET 2023, 1–6. https://doi.org/10.1109/INCET57972.2023.10170499
  11. Saleem, A., Anwar, S., Nawaz, T., Fahad, S., Saud, S., Ur Rahman, T., Khan, M. N. R., & Nawaz, T. (2024). Securing a sustainable future: the climate change threat to agriculture, food security, and sustainable development goals. Journal of Umm Al-Qura University for Applied Sciences. https://doi.org/10.1007/s43994-024-00177-3
  12. Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: a dataset for visual plant disease detection. InProceedings of the 7th ACM IKDD CoDS and 25th COMAD 2020 Jan 5 (pp. 249-253).
  13. Wei, K., Chen, B., Zhang, J., Fan, S., Wu, K., Liu, G., & Chen, D. (2022). Explainable deep learning study for leaf disease classification. Agronomy, 12(5), 1035. https://doi.org/10.3390/agronomy12051035
  14. Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, 30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405