MMDFN: A Multi-modal Deep Fusion Network with Hybrid Optimization for Automated Cotton Leaf Disease Detection

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Author(s)

Mohan Ajmeera 1,* P. Chiranjeevi 2 A. Krishna Mohan 3

1. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Andhra Pradesh, India

2. CSE Department, Amrita Sai Institute of Science and Technology, Bathinapadu, Andhra Pradesh, India

3. CSE Department, University College of Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.04.07

Received: 1 Apr. 2026 / Revised: 15 May 2026 / Accepted: 7 Jul. 2026 / Published: 8 Aug. 2026

Index Terms

Disease Detection, Cotton Plant Leaves, Region Of Interest, Snowy Wolf Optimization, Multi-modal Deep Fusion Network

Abstract

This study presents the Multi-Modal Deep Fusion Network to identify cotton leaf diseases. Initially the images are collected from Kaggle cotton disease dataset. The dataset is preprocessed, and data augmentation is applied exclusively to the training set to prevent data leakage. The VGG-16-based Faster Region-based Convolutional Neural Network model is used for lesion detection and region of interest localization by generating bounding boxes around diseased areas. Both the handcrafted features, shape descriptors and color moments and deep learning features are used in feature extraction. The extracted features are optimized using the Snowy Wolf Optimization algorithm which combines Snow Leopard Optimization and Grey Wolf Optimization. The proposed achieved 98.4% accuracy, 98.6% sensitivity, and 98.8% F-score, consistently outperforming existing methods under identical experimental settings. While the proposed framework demonstrated promising performance on the evaluated dataset, further validation on larger and more diverse field datasets is required to comprehensively assess its generalization capability.

Cite This Paper

Mohan Ajmeera, P. Chiranjeevi, A. Krishna Mohan, "MMDFN: A Multi-modal Deep Fusion Network with Hybrid Optimization for Automated Cotton Leaf Disease Detection", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.4, pp.116-137, 2026. DOI:10.5815/ijisa.2026.04.07

Reference

[1]Memon, M.S., Kumar, P. and Iqbal, R., 2022. Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), p.102.
[2]Patil, B.V. and Patil, P.S., 2021. Computational method for Cotton Plant disease detection of crop management using deep learning and internet of things platforms. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 875-885). Springer Singapore.
[3]Caldeira, R.F., Santiago, W.E. and Teruel, B., 2021. Identification of cotton leaf lesions using deep learning techniques. Sensors, 21(9), p.3169
[4]Liang, X., 2021. Few-shot cotton leaf spots disease classification based on metric learning. Plant Methods, 17, pp.1-11.
[5]Hyder, U. and Talpur, M.R.H., 2024. Detection of cotton leaf disease with machine learning model. Turkish Journal of Engineering, 8(2), pp.380-393.
[6]Ahmad, M., Ullah, F., Hamza, A.R.A., Usman, M., Imran, M., Batyrshin, I., Gelbukh, A. and Sidorov, G., 2024. Cotton Leaf Disease Detection Using Vision Transformers: A Deep Learning Approach. crops, 1, p.3.
[7]Faisal, H.M., Aqib, M., Rehman, S.U., Mahmood, K., Obregon, S.A., Iglesias, R.C. and Ashraf, I., 2025. Detection of cotton crops diseases using customized deep learning model. Scientific Reports, 15(1), p.10766.
[8]Kaur, G., Al‐Yarimi, F.A.M., Bharany, S., Rehman, A.U. and Hussen, S., 2025. Explainable AI for Cotton Leaf Disease Classification: A Metaheuristic‐Optimized Deep Learning Approach. Food Science & Nutrition, 13(7), p.e70658.
[9]Ali, T., Zakir, R., Ayaz, M., Murtaza, M., Hijji, M. and Hadi Aggoune, E.M., 2025. Cotton crop disease detection and classification using statistical prediction model in deep learning approach. Multimedia Tools and Applications, 84(41), pp.49503-49525.
[10]Sibiya, M. and Sumbwanyambe, M., 2021. Automatic fuzzy logic-based maize common rust disease severity predictions with thresholding and deep learning. Pathogens, 10(2), p.131.
[11]Chinnadurai, S. and Selvakumar, S., 2025. Image-based cotton leaf disease diagnosis using YOLO and faster R-CNN techniques. Scientific Reports. 
[12]Salot, P., Pancholi, P., Rathod, H., Rathod, S., Thakkar, M. and Shah, J., 2025. Cotton leaf analysis based early plant disease detection using Machine Learning. Journal of Integrated Science and Technology, 13(1), pp.1015-1015.
[13]Udawant, P. and Srinath, P., 2022. Cotton leaf disease detection using instance segmentation. Journal of Cases on Information Technology (JCIT), 24(4), pp.1-10.
[14]SithaRam, M., Anusha, V., Sri, P.N. and Sri, G.H., 2024, June. A Novel Methodology for Cotton Leaf Disease Detection using CNN. In 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 202-207). IEEE.
[15]Rehman, A., Akhtar, N. and Alhazmi, O.H., 2025. Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models. Scientific Reports, 15(1), p.22570.
[16]Rajasekar, V., Venu, K., Jena, S.R., Varthini, R.J. and Ishwarya, S., 2021. Detection of cotton plant diseases using deep transfer learning. Journal of Mobile Multimedia, 18(2), pp.307-324.
[17]Patil, B.V., Patil, P.S. and Patil, P.R., 2025. HARNN‐IoT: IoT‐Enabled Hybrid Deep Learning Model for Cotton Plant Disease Detection and Classification. Journal of Phytopathology, 173(6), p.e70213.
[18]Jadhav, A., Chaudhari, S., Gupta, A. and Mithbavkar, D.S., 2023. Cotton disease detection and cure using CNN. Int J Res Appl Sci Eng Tech, 11(4), pp.3385-3389.
[19]Ahmed, M.R., 2021. Leveraging convolutional neural network and transfer learning for cotton plant and leaf disease recognition. Int. J. Image Graph. Signal Process, 13(4), pp.47-62.
[20]Wang, Z., Zhang, H.W., Dai, Y.Q., Cui, K., Wang, H., Chee, P.W. and Wang, R.F., 2025. Resource-efficient cotton network: A lightweight deep learning framework for cotton disease and pest classification. Plants, 14(13), p.2082.
[21]He, R., Zhang, F., Zhu, J., Wang, Y., Yang, D., Zhang, T., & Li, P. (2025). YOLOv9-LSBN: An Improved YOLOv9 Model for Cotton Pest and Disease. IEEE Access.
[22]Singh, C., Wibowo, S. and Grandhi, S., 2025. A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO. Applied Sciences, 15(13), p.7075.
[23]Rahman, M.A., Ullah, M.S., Devnath, R.K., Chowdhury, T.H., Rahman, G. and Rahman, M.A., 2025. Cotton leaf disease detection: an integration of CBAM with deep learning approaches. Int J Comput Appl, 975, p.8887.
[24]Pandiyaraju, V., Anusha, B., Senthil Kumar, A.M., Jaspin, K., Venkatraman, S. and Kannan, A., 2025. Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection. Neural Computing and Applications, 37(14), pp.8309-8329.
[25]Joshi, K., Yadav, Y., Hooda, S., Nandal, R., Singh, B., Singh, K., Tuteja, N., Gill, R. and Gill, S.S., 2025. Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture. Scientific Reports, 15(1), p.35602.
[26]Neelavathi, G. and Venkatasalam, K., 2026. A novel Hybrid Vision Transformer with dense attention capsule network (HVT-DACapNet) model for cotton plant disease detection. Scientific Reports.
[27]Aslan, E. and Özüpak, Y., 2026. A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification. BMC Plant Biology.
[28]Sarwar, R., Aslam, M., Khurshid, K.S., Ahmed, T., Martinez-Enriquez, A.M. and Waheed, T., 2021. Detection and classification of cotton leaf diseases using faster R-CNN on field condition images. Acta Sci Agric, 5(10). 
[29]Shrotriya, A., Sharma, A.K., Bairwa, A.K. and Manoj, R., 2024. Hybrid ensemble learning with CNN and RNN for multimodal cotton plant disease detection. IEEE Access, 12, pp.198028-198045. 
[30]Guo, J., Ye, W., Wang, D., He, Z., Yan, Z., Sato, M., & Sato, Y. (2024). A novel snow leopard optimization for high-dimensional feature selection problems. Sensors, 24(22), 7161.
[31]Gülmez, B., 2023. A novel deep learning model with the Grey Wolf Optimization algorithm for cotton disease detection. JOURNAL OF UNIVERSAL COMPUTING SCIENCE
[32]Jashim, F.B., Refat, F.R., Karim, M.H., Mahmud, F.U. and Ashrafi, F., 2025. A web-based application for cotton leaf disease classification using vision transformer. Int J Sci Res Arch, 15(2), pp.1405-16. 
[33]http://kaggle.com/datasets/sabuktagin/dataset-for-cotton-leaf-disease-detection
[34]https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset