Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

作     者：Mona Jamjoom Ahmed Elhadad Hussein Abulkasim Safia Abbas 

作者机构：Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer ScienceFaculty of Computers and InformationSouth Valley UniversityQena83523Egypt Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Computer ScienceFaculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第7期

页      面：367-382页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R104) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：SVM machine learning GLCM algorithm K-means clustering LBP 

摘      要：Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pretty challenging in this *** processing is employed to detect plant disease since it requires much effort and an extended processing *** 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 ***,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 *** gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant *** to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.