Ince Ibrahim Furkan, Shehu Harisu Abdullahi, Osmani Shaira, Bulut Faruk
Department of Software Engineering, Istinye University, Istanbul, Türkiye.
School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
Front Plant Sci. 2025 Aug 29;16:1624373. doi: 10.3389/fpls.2025.1624373. eCollection 2025.
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.
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.
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%).
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.
早期检测豆类叶片上的病害对于防止农业生产力下降和缓解更广泛的农业挑战至关重要。然而，一些豆类叶片病害即使肉眼也难以检测，这给依赖精确特征提取的机器学习方法带来了重大挑战。
我们提出了一种新颖的方法，即离散余弦变换变压器（DCT-Transformers），它将一种预处理技术——动态范围增强离散余弦变换（DRE-DCT）与基于变压器的模型相结合。DRE-DCT方法通过提取通常难以察觉的高频分量和细微细节来增强输入图像的动态范围，同时保持整体图像质量。然后使用变压器模型对应用此预处理步骤前后的豆类叶片图像进行分类。
实验评估表明，所提出的DCT-Transformers方法在使用预处理图像时实现了99.56%的分类准确率（精确率：0.9916，召回率：0.9912，F1分数：0.9912），而使用未预处理图像时为95.92%。此外，该方法优于现有最先进的方法（均低于94%）和类似研究（均低于98.5%）。
这些发现表明，通过DRE-DCT增强特征提取可显著提高病害分类性能。所提出的方法为农业中的早期病害检测提供了一种有效的解决方案，有助于改进病害管理策略并支持粮食安全倡议。
