A novel transformer using dynamic range-enhanced discrete cosine transform for detecting bean leaf diseases.

Abstract

Introduction Early detection of diseases on bean leaves is essential for preventing declines in agricultural productivity and mitigating broader agricultural challenges. However, some bean leaf diseases are difficult to detect even with the human eye, posing significant challenges for machine learning methods that rely on precise feature extraction. Methods We propose a novel approach, DCT-Transformers, which combines a preprocessing technique, dynamic range enhanced discrete cosine transform (DRE-DCT) with Transformer-based models. The DRE-DCT method enhances the dynamic range of input images by extracting high-frequency components and subtle details that are typically imperceptible while preserving overall image quality. Transformer models were then used to classify bean leaf images before and after applying this preprocessing step. Results Experimental evaluations demonstrate that the proposed DCT-Transformers method achieved a classification accuracy of 99.56% (precision: 0.9916, recall: 0.9912, F1-score: 0.9912) when using preprocessed images, compared to 95.92% when using non-preprocessed images. Moreover, the method outperformed state-of-the-art approaches (all below 94%) and similar studies (all below 98.5%). Discussion These findings indicate that enhancing feature extraction through DRE-DCT significantly improves disease classification performance. The proposed method offers an efficient solution for early disease detection in agriculture, contributing to improved disease management strategies and supporting food security initiatives.

Code and data availability

The paper's data availability statement explicitly links the iBean leaf disease image dataset used for all experiments and the authors' public GitHub repository containing the study's implementation code.