DENSENET169-ENABLED HIGH-ACCURACY AUTOMATED DETECTION SYSTEM FOR COTTON LEAF DISEASES USING DEEP TRANSFER LEARNING
AUTHORS:
Mentor
Dr.G.Thirupati
Affiliation
Department Of CSE, SVS Group of Institutions (Autonomous), Bheemaram ,Hanmakonda, Telangana
DOI: 
SHARE THIS ARTICLE
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Abstract
Rapid and accurate identification of cotton foliar diseases which seriously threatens cotton output worldwide is of great importance. A strong deep learning model with custom architecture DenseNet169 is proposed in this research for automatic classification of seven diseases of cotton leaf: Bacterial Blight, Curl Virus, healthy leaf, herbicide growth damage, leaf hopper jassids, leaf redding and leaf variegation. We proposed a two-step transfer learning method with enhanced data augmentation based on the SAR-CLD–2024 dataset, which contains 9,137 images. The DenseNet169 architecture proposed here yielded a remarkable performance with a validation accuracy of 96.83% while precision, recall, and F1-score were 96.89%, 96.93%, and 96.90%, respectively, with a significant enhancement than prior related approaches. It gets flawless classification for Herbicide Growth Damage and close to perfect for each disease types with macro-average AUC of 99.81%. The second is the parameters of the deep architecture that we adapted for agricultural pathology where we were broadly successful at systematic feature extraction and then fine-tuning 161 layers. The new high-water mark physiological plant disease diagnosis that we establish here is an important step toward ultimately real-world applicability as adaptive components of precision agriculture to monitor crop health and protect yield.
Keywords
Cotton Leaf Disease Classification DenseNet169 Deep TransferLearning Precision Agriculture Computer Vision Plant Pathology Automated Disease Detection Convolutional Neural Networks
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 8
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Aug 24 2026
Article Metrics
Article Views
60
PDF Downloads
0
HOW TO CITE
Copy
Kumar, R. (2026). DenseNet169-Enabled High-Accuracy Automated Detection System for Cotton Leaf Diseases Using Deep Transfer Learning. International Journal of Science, Strategic Management and Technology, 02(8), 1-9. https://doi.org/10.55041/ijsmt.v2i8.064
Kumar, Rebally.Vijay. "DenseNet169-Enabled High-Accuracy Automated Detection System for Cotton Leaf Diseases Using Deep Transfer Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i8.064.
Kumar, Rebally.Vijay. "DenseNet169-Enabled High-Accuracy Automated Detection System for Cotton Leaf Diseases Using Deep Transfer Learning." International Journal of Science, Strategic Management and Technology 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i8.064.
References
1.Wang, Z., Zhang, H. W., Dai, Y. Q., Cui, K., Wang, H., Chee, P. W., & Wang, R. F. (2025). Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification. Plants, 14(13), 2082.
2.Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton disease detection based on deep learning techniques. In 4th Smart Cities Symposium (SCS 2021) (Vol. 2021, pp. 496-501). IET.
3.Ali, T., Zakir, R., Ayaz, M., Murtaza, M., Hijji, M., & Hadi Aggoune, E. M. (2025). Cotton crop disease detection and classification using statistical prediction model in deep learning approach. Multimedia Tools and Applications, 1-23.
4.Latif, M. R., Khan, M. A., Javed, M. Y., Masood, H., Tariq, U., & Kadry, S. (2021). Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm. Computers, Materials & Continua, 69(3).
5.Islam, M. M., Talukder, M. A., Sarker, M. R. A., Uddin, M. A., Akhter, A., Sharmin, S., ... & Debnath, S. K. (2023). A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications, 20, 200278.
6.Zekiwos, M., & Bruck, A. (2021). Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis. Journal of Electrical & Computer Engineering.
7.Caldeira, R. F., Santiago, W. E., & Teruel, B. (2021). Identification of cotton leaf lesions using deep learning techniques. Sensors, 21(9), 3169.
8.Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102.
9.Kumar, S., Jain, A., Shukla, A. P., Singh, S., Raja, R., Rani, S., ... & Masud, M. (2021). A comparative analysis of machine learning algorithms for detection of organic and nonorganic cotton diseases. Mathematical Problems in Engineering, 2021(1), 1790171.
10.Iqbal, S., Ayaz, A., Qabulio, M., Memon, M. S., & Nizamani, S. (2024). A Deep Learning Based Model for the Classification of Cotton Crop Disease. Technical Journal, 29(01), 61-68.
2.Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton disease detection based on deep learning techniques. In 4th Smart Cities Symposium (SCS 2021) (Vol. 2021, pp. 496-501). IET.
3.Ali, T., Zakir, R., Ayaz, M., Murtaza, M., Hijji, M., & Hadi Aggoune, E. M. (2025). Cotton crop disease detection and classification using statistical prediction model in deep learning approach. Multimedia Tools and Applications, 1-23.
4.Latif, M. R., Khan, M. A., Javed, M. Y., Masood, H., Tariq, U., & Kadry, S. (2021). Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm. Computers, Materials & Continua, 69(3).
5.Islam, M. M., Talukder, M. A., Sarker, M. R. A., Uddin, M. A., Akhter, A., Sharmin, S., ... & Debnath, S. K. (2023). A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications, 20, 200278.
6.Zekiwos, M., & Bruck, A. (2021). Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis. Journal of Electrical & Computer Engineering.
7.Caldeira, R. F., Santiago, W. E., & Teruel, B. (2021). Identification of cotton leaf lesions using deep learning techniques. Sensors, 21(9), 3169.
8.Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102.
9.Kumar, S., Jain, A., Shukla, A. P., Singh, S., Raja, R., Rani, S., ... & Masud, M. (2021). A comparative analysis of machine learning algorithms for detection of organic and nonorganic cotton diseases. Mathematical Problems in Engineering, 2021(1), 1790171.
10.Iqbal, S., Ayaz, A., Qabulio, M., Memon, M. S., & Nizamani, S. (2024). A Deep Learning Based Model for the Classification of Cotton Crop Disease. Technical Journal, 29(01), 61-68.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
A Study on Investment Preferences of Rural and Urban Customers Towards Fixed Deposits with Special Reference to ICICI Bank
(2026)
DOI: 10.55041/ijsmt.v2i5.473
Deep Learning for Waste Classification
(2026)
DOI: 10.55041/ijsmt.v2i5.197
Hospital Management System
(2026)
DOI: 10.55041/ijsmt.v2i8.049
Previous Article
