ARCHIVES
Original Article
Plant Leaf Disease Detection Using Transfer Learning Approach
N UshaSree1
Likhith Kumar D N2
Karthik R3
Nithin V4
1 Professor, Department of Computer Science & Engineering Rajarajeswari College of Engineering Bangalore, Karnataka, India. 2 3 4 Department of Computer Science & Engineering Rajarajeswari College of Engineering Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 125-134
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506019
References
1. M. Adi, A. K. Singh, H. Reddy A, Y. Kumar, V. R. Challa, P. Rana, and U. Mittal, ‘‘An overview on plant disease detection algorithm using deep learning,’’ in Proc. 2nd Int. Conf. Intell. Eng. Manage. (ICIEM), Apr. 2021, pp. 305–309.
2. R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, ‘‘Performance of deep learning vs machine learning in plant leaf disease detection,’’ Microprocessors Microsystems, vol. 80, Feb. 2021, Art. no. 103615.
3. W. Shafik, A. Tufail, A. Namoun, L. C. De Silva, and R. H. M. Apong, ‘‘A systematic literature review on plant disease detection: Motivations, classification techniques, datasets, challenges, and future trends,’’ IEEE Access, vol. 11, pp. 59174–59203, 2023
4. D. P. Hughes and M. Salathe, ‘‘An open access repository of images on plant health to enable the development of mobile disease diagnostics,’’ 2015, arXiv: 1511.08060.
5. (2020). IBean. Accessed: Mar. 31, 2022. [Online]. Available:https://github.com/AILabMakerere/ibean/blob/ master/README.md.
6. M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, and M. Y. Javed, ‘‘Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection,’’ Comput. Electron. Agricult., vol. 150, pp. 220–234, Jul. 2018.
7. (2020). Rice Leaf Disease Image Samples. Accessed: Apr.1,2022.[Online].Available:https://data.mendeley.com/ datasets/fwcj7stb8r/1.
8. D. O. Oyewola, E. G. Dada, S. Misra, and R. Damasevicius, ‘‘Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing,’’ PeerJ Comput. Sci., vol. 7, p. e352, Mar. 2021.
9. (2018). AI Challenger 2018 Datasets. Accessed: Apr. 1,2022.[Online].Available:https://github.com/AIChallenge r/AI_Challenger_2018 [10] M. Ahmad, M. Abdullah, H. Moon, and D. Han, ‘‘Plant disease detection in imbalanced datasets using efficient convolutional neu ral networks with stepwise transfer learning,’’ IEEE Access, vol. 9, pp. 140565–140580, 2021.
10. D. Wang, J. Wang, Z. Ren, and W. Li, ‘‘DHBP: A dual-stream hierarchical bilinear pooling model for plant disease multi-task classification,’’ Comput. Electron. Agricult., vol. 195, Apr. 2022, Art. no. 106788.
11. L. C. Ngugi, M. Abdelwahab, and M. Abo-Zahhad, ‘‘Tomato leaf seg mentation algorithms for mobile phone applications using deep learning,’’ Comput. Electron. Agricult., vol. 178, Nov. 2020, Art. no. 105788.
2. R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, ‘‘Performance of deep learning vs machine learning in plant leaf disease detection,’’ Microprocessors Microsystems, vol. 80, Feb. 2021, Art. no. 103615.
3. W. Shafik, A. Tufail, A. Namoun, L. C. De Silva, and R. H. M. Apong, ‘‘A systematic literature review on plant disease detection: Motivations, classification techniques, datasets, challenges, and future trends,’’ IEEE Access, vol. 11, pp. 59174–59203, 2023
4. D. P. Hughes and M. Salathe, ‘‘An open access repository of images on plant health to enable the development of mobile disease diagnostics,’’ 2015, arXiv: 1511.08060.
5. (2020). IBean. Accessed: Mar. 31, 2022. [Online]. Available:https://github.com/AILabMakerere/ibean/blob/ master/README.md.
6. M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, and M. Y. Javed, ‘‘Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection,’’ Comput. Electron. Agricult., vol. 150, pp. 220–234, Jul. 2018.
7. (2020). Rice Leaf Disease Image Samples. Accessed: Apr.1,2022.[Online].Available:https://data.mendeley.com/ datasets/fwcj7stb8r/1.
8. D. O. Oyewola, E. G. Dada, S. Misra, and R. Damasevicius, ‘‘Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing,’’ PeerJ Comput. Sci., vol. 7, p. e352, Mar. 2021.
9. (2018). AI Challenger 2018 Datasets. Accessed: Apr. 1,2022.[Online].Available:https://github.com/AIChallenge r/AI_Challenger_2018 [10] M. Ahmad, M. Abdullah, H. Moon, and D. Han, ‘‘Plant disease detection in imbalanced datasets using efficient convolutional neu ral networks with stepwise transfer learning,’’ IEEE Access, vol. 9, pp. 140565–140580, 2021.
10. D. Wang, J. Wang, Z. Ren, and W. Li, ‘‘DHBP: A dual-stream hierarchical bilinear pooling model for plant disease multi-task classification,’’ Comput. Electron. Agricult., vol. 195, Apr. 2022, Art. no. 106788.
11. L. C. Ngugi, M. Abdelwahab, and M. Abo-Zahhad, ‘‘Tomato leaf seg mentation algorithms for mobile phone applications using deep learning,’’ Comput. Electron. Agricult., vol. 178, Nov. 2020, Art. no. 105788.
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