Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation |
| |
Authors: | Mona Jamjoom Ahmed Elhadad Hussein Abulkasim Safia Abbas |
| |
Affiliation: | 1.Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia2 Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt3 Faculty of Science, New Valley University, El-Kharga, 72511, Egypt4 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt |
| |
Abstract: | Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum, Puccinia graminis, tomato yellow leaf curl. Therefore, this work uses the Support vector machine (SVM) classifier to detect and classify the plant disease using various steps like image acquisition, Pre-processing, Segmentation, feature extraction, and classification. The gray level co-occurrence matrix (GLCM) and the local binary pattern features (LBP) are used to identify the disease-affected portion of the plant leaf. According to experimental data, the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. |
| |
Keywords: | SVM machine learning GLCM algorithm K-means clustering LBP |
|
| 点击此处可从《计算机、材料和连续体(英文)》浏览原始摘要信息 |
|
点击此处可从《计算机、材料和连续体(英文)》下载全文 |
|