Alsakar Yasmin M, Sakr Nehal A, Elmogy Mohammed
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
Sci Rep. 2024 Dec 23;14(1):30601. doi: 10.1038/s41598-024-81143-1.
The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases. Moreover, more information can be extracted from the input images due to different feature extraction techniques. This paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC). The proposed system consists of five stages. First, input images acquire RGB images of rice plants. Second, image preprocessing applies data augmentation to solve imbalanced problems, and logarithmic transformation enhancement to handle illumination problems has been applied. Third, the features extraction stage is responsible for extracting color features using CC and color texture features using multi-level multi-channel local binary pattern (MCLBP). Fourth, the feature fusion stage provides complementary and discriminative information by concatenating the two types of features. Finally, the rice image classification stage has been applied using a one-against-all support vector machine (SVM). The proposed system has been evaluated on three benchmark datasets with six classes: Blast (BL), Bacterial Leaf Blight (BLB), Brown Spot (BS), Tungro (TU), Sheath Blight (SB), and Leaf Smut (LS) have been used. Rice Leaf Diseases First Dataset, Second Dataset, and Third Dataset achieved maximum accuracy of 99.53%, 99.4%, and 99.14%, respectively, with processing time from [Formula: see text]. Hence, the proposed system has achieved promising results compared to other state-of-the-art approaches.
水稻是世界上最重要的作物之一，并且会遭受多种病害。传统的水稻病害检测方法复杂且耗时，主要依赖专家经验。图像处理、计算机视觉和深度学习技术的迅猛发展为自动检测和分类这些病害提供了有效且创新的农业解决方案。此外，由于采用了不同的特征提取技术，可以从输入图像中提取更多信息。本文提出了一种通过融合不同特征来检测和分类水稻叶片病害的新系统，这些特征包括使用局部二值模式（LBP）的颜色纹理和使用颜色相关图（CC）的颜色特征。所提出的系统包括五个阶段。首先，输入图像获取水稻植株的RGB图像。其次，图像预处理应用数据增强来解决不平衡问题，并应用对数变换增强来处理光照问题。第三，特征提取阶段负责使用CC提取颜色特征，并使用多级多通道局部二值模式（MCLBP）提取颜色纹理特征。第四，特征融合阶段通过连接这两种类型的特征来提供互补和有区分力的信息。最后，使用一对多支持向量机（SVM）进行水稻图像分类阶段。所提出的系统在三个包含六个类别的基准数据集上进行了评估：稻瘟病（BL）、白叶枯病（BLB）、褐斑病（BS）、东格鲁病（TU）、纹枯病（SB）和叶黑粉病（LS）。水稻叶部病害第一数据集、第二数据集和第三数据集分别实现了99.53%、99.4%和99.14%的最大准确率，处理时间为[公式：见原文]。因此，与其他现有先进方法相比，所提出的系统取得了有前景的结果。
