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

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.