Araştırma Makalesi
Yıl 2025,
Cilt: 12 Sayı: 3, 679 - 686, 23.07.2025
Öz
Kaynakça
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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).
- 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
- 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
Comparative Performance Analysis of Deep Learning Based CNN Models for Diagnosis of Corn Leaf Diseases
Öz
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.
Anahtar Kelimeler
: Deep learning corn leaf diseases CNN image classification ROC-AUC precision
Kaynakça
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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).
- 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
- 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
Toplam 14 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Ziraat Mühendisliği (Diğer) |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 23 Temmuz 2025 |
| Gönderilme Tarihi | 8 Mayıs 2025 |
| Kabul Tarihi | 13 Haziran 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 12 Sayı: 3 |
