ARCHIVES
Original Article
An Effective Leaf Disease Detection System for Smart Farming
Deepanshu Bhardwaj1
Dheeraj Chillar2
1 P.G. Student, Department of CSE, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India. 2 Director, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India.
Published Online: March-April 2026
Pages: 196-202
Cite this article
No DOIReferences
1. Singh, A., Arora, M., Sharma, R., 2025, Automated Leaf Disease Identification via Image Processing and Multi-Class Support Vector
Machine, IJIRSET, Vol. 14, Issue 6, pp. 16450-16459.
2. Singh, A., Arora, M., Sharma, R., 2025, Image Processing and Machine Learning Approaches for Leaf Disease Identification: A Survey,
EXCELLENCIA: INTERNATIONAL MULTI-DISCIPLINARY JOURNAL OF EDUCATION, Volume 3, Issue 6, 2025.
3. Mahlein, A.-K. (2016). Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture and plant
phenotyping. Plant Disease, 100(2), 241–251.4. Barbedo, J.G.A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1),
660.
5. Phadikar, S., Sil, J., & Paul, M. (2013). Rice diseases classification using feature selection and rule generation techniques. Computers and
Electronics in Agriculture, 90, 76–85.
6. Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh, Z. (2011). Fast and accurate detection and classification of plant
diseases. International Journal of Computer Applications, 17(1), 31–38.
7. Arivazhagan, S., Newlin Shebiah, R., Ananthi, S., & Vishnu Varthini, S. (2013). Detection of unhealthy region of plant leaves and
classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211–217.
8. Camargo, A., & Smith, J. S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms.
Biosystems Engineering, 102(1), 9–21.
9. Mohanty, S.P., Hughes, D.P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science,
7, 1419.
10. Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2016). Image processing-based detection of fungal diseases in plants. Procedia Computer
Science, 46, 1802–1808.
11. Patil, S. B., & Kumar, R. (2011). Feature extraction of diseased leaf images. International Journal of Advanced Computer Science and
Applications, 2(6), 130–134.
12. Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and
Electronics in Agriculture, 72(1), 1–13.
13. Zhang, S., Wu, X., & You, Z. (2017). Leaf image based cucumber disease recognition using sparse representation classification. Computers
and Electronics in Agriculture, 134, 135–141.
14. Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases
with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
Machine, IJIRSET, Vol. 14, Issue 6, pp. 16450-16459.
2. Singh, A., Arora, M., Sharma, R., 2025, Image Processing and Machine Learning Approaches for Leaf Disease Identification: A Survey,
EXCELLENCIA: INTERNATIONAL MULTI-DISCIPLINARY JOURNAL OF EDUCATION, Volume 3, Issue 6, 2025.
3. Mahlein, A.-K. (2016). Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture and plant
phenotyping. Plant Disease, 100(2), 241–251.4. Barbedo, J.G.A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1),
660.
5. Phadikar, S., Sil, J., & Paul, M. (2013). Rice diseases classification using feature selection and rule generation techniques. Computers and
Electronics in Agriculture, 90, 76–85.
6. Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh, Z. (2011). Fast and accurate detection and classification of plant
diseases. International Journal of Computer Applications, 17(1), 31–38.
7. Arivazhagan, S., Newlin Shebiah, R., Ananthi, S., & Vishnu Varthini, S. (2013). Detection of unhealthy region of plant leaves and
classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211–217.
8. Camargo, A., & Smith, J. S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms.
Biosystems Engineering, 102(1), 9–21.
9. Mohanty, S.P., Hughes, D.P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science,
7, 1419.
10. Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2016). Image processing-based detection of fungal diseases in plants. Procedia Computer
Science, 46, 1802–1808.
11. Patil, S. B., & Kumar, R. (2011). Feature extraction of diseased leaf images. International Journal of Advanced Computer Science and
Applications, 2(6), 130–134.
12. Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and
Electronics in Agriculture, 72(1), 1–13.
13. Zhang, S., Wu, X., & You, Z. (2017). Leaf image based cucumber disease recognition using sparse representation classification. Computers
and Electronics in Agriculture, 134, 135–141.
14. Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases
with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
Related Articles
2026
Fake Currency Detection Using Deep Learning
2026
Smart E-Commerce System with Dynamic Pricing
2026
Personal Expense Tracker with Currency Converter
2026
Paw Safe: An Extensive Technology-Driven Framework for Stray Dog Rescue, Healthcare Management, Community Engagement, and Smart Urban Governance
2026
Design and Development of a Full-Stack E-Commerce Website
2026
