Enhanced wheat crop leaf disease classification using multi-level contrast enhancement and modified vision transformers.

The integration of advanced tools and techniques has significantly boosted agricultural productivity. Wheat crops, which are vital for global food security, are often susceptible to various bacterial and viral diseases, considerably impacting both yield and quality. Efficient disease detection is crucial for effective treatment and yield optimization. This study presents an innovative approach that combines a multi-level contrast enhancement framework with a novel transformer-based architecture for the rapid and precise diagnosis of wheat crop leaf diseases. By employing contrast enhancement techniques, we enhance the visual quality of wheat crop images, facilitating improved feature extraction. Incorporating Vision Transformers (ViTs) enhances computational efficiency, enables multi-scale feature extraction, and reduces dimensionality. We implement and evaluate our proposed models on two publicly available wheat datasets, utilizing three variants of ViT: a modified ViT with seven-block transformers, a pre-trained ViT-16-Tiny, and a modified ViT with seven transformer blocks and skip connections. Our results demonstrate classification accuracies of 98.90% for the modified seven-block ViT, 97.50% for the ViT-16-Tiny model, and 97.90% for the ViT seven-block with skip connections. A comparative analysis with state-of-the-art techniques reveals that our proposed techniques outperform existing methods in terms of accuracy, precision, sensitivity, False Negative Rate, and the total number of learnable parameters.