TY  - EJOU
AU  - Jamjoom, Mona 
AU  - Elhadad, Ahmed 
AU  - Abulkasim, Hussein 
AU  - Abbas, Safia 

TI  - Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 76
IS  - 1
SN  - 1546-2226

AB  - 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 <i>Phytophthora infestans</i>, 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.
KW  - SVM; machine learning; GLCM algorithm; K-means clustering; LBP

DO  - 10.32604/cmc.2023.037310