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

Ibrahim Furkan Ince ¹, Harisu Abdullahi Shehu ², Shaira Osmani ³

⁰Department of Software Engineering, Istinye University, Istanbul, Türkiye.

Frontiers in Plant Science +

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September 15, 2025

View abstract on PubMed

Summary

Early detection of bean leaf diseases is crucial. A new method, DCT-Transformers, uses dynamic range enhanced discrete cosine transform (DRE-DCT) for improved machine learning classification accuracy, aiding agricultural disease management.

Area Of Science

Agricultural Science
Computer Vision
Machine Learning

Background

Early detection of bean leaf diseases is vital for agricultural productivity.
Subtle disease indicators can be challenging for human and machine detection.
Effective disease identification supports crop management and food security.

Purpose Of The Study

To develop an advanced machine learning approach for accurate bean leaf disease classification.
To enhance feature extraction for improved disease detection in agriculture.
To address limitations in current machine learning methods for plant pathology.

Main Methods

Proposed DCT-Transformers method combining dynamic range enhanced discrete cosine transform (DRE-DCT) preprocessing with Transformer models.
DRE-DCT enhances image dynamic range by extracting high-frequency and subtle details.
Transformer models classify bean leaf images pre- and post-DRE-DCT preprocessing.

Main Results

DCT-Transformers achieved 99.56% classification accuracy with preprocessed images, significantly higher than 95.92% with non-preprocessed images.
The method outperformed existing state-of-the-art approaches (below 94%) and similar studies (below 98.5%).
Enhanced feature extraction via DRE-DCT demonstrably improved disease classification performance.

Conclusions

The proposed DCT-Transformers method offers an efficient and highly accurate solution for early bean leaf disease detection.
Improved feature extraction is key to advancing machine learning in agricultural disease diagnostics.
This approach contributes to better disease management strategies and global food security.

Keywords:

agricultural informatics bean leaf diseases classification frequency weighting. 32 33 image 31 preprocessing image classification transformer deep learning model