ARCHIVES
Research Article
Wheat Rust Disease Detection Using Convolutional Neural Network
Ch Biswaranjan Nanda1
Sudhir Kumar Mohapatra2
Rabi Narayan Satpathy3
123 Faculty of Emerging Technologies, Sri Sri University, Cuttack, Odisha, India. *Corresponding Author
Published Online: September-December 2024
Pages: 11-16
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20240303004References
1. ReliefWeb, “Ethiopia battles wheat rust disease outbreak in critical wheat-growing regions,” 2016. [Online]. Available:
https://reliefweb.int/report/ethiopia/ethiopia-battles-wheat-rust-disease-outbreak-critical-wheat-growing-regions.
2. GreenLife, “Wheat Rust (1).” [Online]. Available: https://www.greenlife.co.ke/wheat-rust/.
3. S. N. Wegulo, E. P. Pathologist, and E. Byamukama, “Rust Diseases of Wheat,” 2000. [Online]. Available:
https://ohioline.osu.edu/factsheet/plpath-cer-12.
4. Kolomsa Agricultural Research Center, “No Title,” 2019.
5. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep Learning for Image-Based Cassava Disease
Detection,” Front. Plant Sci., vol. 8, no. 2002, pp. 1–10, 2017.
6. S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. 7,
2016.
7. K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, no. January, pp.
311–318, 2018.
8. E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,”
Comput. Electron. Agric., vol. 161, no. October 2017, pp. 272–279, 2019.
9. Picon, A. Alvarez-Gila, M. Seitz, A. Ortiz-Barredo, J. Echazarra, and A. Johannes, “Deep convolutional neural networks for mobile capture
device-based crop disease classification in the wild,” Comput. Electron. Agric., vol. 161, no. October 2017, pp. 280–290, 2019.
10. Mohapatra, Sudhir Kumar, Srinivas Prasad, and Sarat Chandra Nayak. "Wheat Rust Disease Detection Using Deep Learning." Data
Science and Data Analytics: Opportunities and Challenges (2021): 191.
11. Sinshaw, Natnael Tilahun, et al. "Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature
Review." Computational Intelligence and Neuroscience 2022 (2022).
12. Sinshaw, Natnael Tilahun, Beakal Gizachew Assefa, and Sudhir Kumar Mohapatra. "Transfer Learning and Data Augmentation Based
CNN Model for Potato Late Blight Disease Detection." 2021 International Conference on Information and Communication Technology
for Development for Africa (ICT4DA). IEEE, 2021.
13. Mohapatra, Sudhir Kumar. "Automatic Lung Tuberculosis Detection Model Using Thorax Radiography Image." Deep Learning
Applications in Medical Imaging. IGI Global, 2021. 223-242.
14. Mekonnen, Adem Assfaw, et al. "Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning."
Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021. 119-137.
https://reliefweb.int/report/ethiopia/ethiopia-battles-wheat-rust-disease-outbreak-critical-wheat-growing-regions.
2. GreenLife, “Wheat Rust (1).” [Online]. Available: https://www.greenlife.co.ke/wheat-rust/.
3. S. N. Wegulo, E. P. Pathologist, and E. Byamukama, “Rust Diseases of Wheat,” 2000. [Online]. Available:
https://ohioline.osu.edu/factsheet/plpath-cer-12.
4. Kolomsa Agricultural Research Center, “No Title,” 2019.
5. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep Learning for Image-Based Cassava Disease
Detection,” Front. Plant Sci., vol. 8, no. 2002, pp. 1–10, 2017.
6. S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. 7,
2016.
7. K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, no. January, pp.
311–318, 2018.
8. E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,”
Comput. Electron. Agric., vol. 161, no. October 2017, pp. 272–279, 2019.
9. Picon, A. Alvarez-Gila, M. Seitz, A. Ortiz-Barredo, J. Echazarra, and A. Johannes, “Deep convolutional neural networks for mobile capture
device-based crop disease classification in the wild,” Comput. Electron. Agric., vol. 161, no. October 2017, pp. 280–290, 2019.
10. Mohapatra, Sudhir Kumar, Srinivas Prasad, and Sarat Chandra Nayak. "Wheat Rust Disease Detection Using Deep Learning." Data
Science and Data Analytics: Opportunities and Challenges (2021): 191.
11. Sinshaw, Natnael Tilahun, et al. "Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature
Review." Computational Intelligence and Neuroscience 2022 (2022).
12. Sinshaw, Natnael Tilahun, Beakal Gizachew Assefa, and Sudhir Kumar Mohapatra. "Transfer Learning and Data Augmentation Based
CNN Model for Potato Late Blight Disease Detection." 2021 International Conference on Information and Communication Technology
for Development for Africa (ICT4DA). IEEE, 2021.
13. Mohapatra, Sudhir Kumar. "Automatic Lung Tuberculosis Detection Model Using Thorax Radiography Image." Deep Learning
Applications in Medical Imaging. IGI Global, 2021. 223-242.
14. Mekonnen, Adem Assfaw, et al. "Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning."
Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021. 119-137.
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