Raman Ramesh, Jayaraman Sangeetha
Department of CSE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, India.
Sci Rep. 2025 Mar 27;15(1):10595. doi: 10.1038/s41598-025-94891-5.
Rice is susceptible to various diseases, including brown spot, hispa, leaf smut, bacterial leaf blight, and leaf blast, all of which can negatively impact crop yields. Current disease detection methods encounter several challenges, such as reliance on a single dataset that diminishes accuracy, the use of complex models, and the limitations posed by small datasets that hinder performance. To overcome these challenges, this paper presents a novel hybrid deep learning (DL) approach for classifying rice leaf diseases. The proposed model leverages two distinct datasets: the Rice Leaf Diseases Dataset and the Rice Disease Images Dataset. It enhances image quality through two advanced techniques: Upgraded Weighted Median Filtering (Up-WMF) to minimize noise and Aligned Gamma-based Contrast Limited Adaptive Histogram Equalization (AG-CLAHE) to improve image contrast. Features from these images are extracted using methods such as Discrete Wavelet Transform (DWT), Gray Level Run Length Matrix (GLRLM), and deep learning-based VGG19 features. To optimize model performance, the most significant features are selected using the Bio-Inspired Artificial Hummingbird (BI-AHB) method, which streamlines complexity. Classification of rice diseases is conducted using a new model known as the Dual Branch Convolutional Graph Attention Neural Network (DB-CGANNet). This model demonstrates remarkable performance, achieving 98.9% accuracy on rice leaf disease dataset and 99.08% on Rice diseases image, surpassing existing techniques. The proposed methodology enhances disease detection accuracy, facilitating improved management of rice crops and contributing to increased agricultural productivity.
水稻易受多种病害影响，包括褐斑病、稻叶铁甲、叶黑粉病、细菌性叶枯病和叶瘟病，所有这些病害都会对作物产量产生负面影响。当前的病害检测方法面临若干挑战，例如依赖单一数据集会降低准确性、使用复杂模型以及小数据集带来的限制会阻碍性能。为克服这些挑战，本文提出一种用于对水稻叶部病害进行分类的新型混合深度学习（DL）方法。所提出的模型利用两个不同的数据集：水稻叶部病害数据集和水稻病害图像数据集。它通过两种先进技术提高图像质量：升级加权中值滤波（Up-WMF）以最小化噪声，以及基于对齐伽马的对比度受限自适应直方图均衡化（AG-CLAHE）以改善图像对比度。使用离散小波变换（DWT）、灰度游程长度矩阵（GLRLM）和基于深度学习的VGG19特征等方法从这些图像中提取特征。为优化模型性能，使用受生物启发的人工蜂鸟（BI-AHB）方法选择最重要的特征，从而简化复杂度。使用一种名为双分支卷积图注意力神经网络（DB-CGANNet）的新模型对水稻病害进行分类。该模型表现出卓越的性能，在水稻叶部病害数据集上的准确率达到98.9%，在水稻病害图像数据集上达到99.08%，超过现有技术。所提出的方法提高了病害检测的准确性，有助于改善水稻作物的管理并提高农业生产力。
