Performance Comparison between SVM and LS-SVM for Rice Leaf Disease detection

Abstract

INTRODUCTION: Automatic detection of rice plant diseases at early stage from its images is quite beneficial over traditional verification methods. OBJECTIVES: Recent years machine learning (ML) approaches are more efficient in disease classification task. In current generation the statistical machine learning algorithm which shows state-of-arts performance is Support Vector Machine (SVM) and variants of SVM. METHODS: SVM has an excellent learning performance for linear and non-linear data samples. It works for Quadratic Programming Problems (QPP) due to which it has the drawback of computational complexity. However QPP can be solved linearly with the help of Least Square SVM(LS-SVM) approach. In LS-SVM the epsilon tube and slack variables of SVM are replaced with error variables. The distance is calculated by error square value. RESULTS: In this research performance comparison is made between SVM and LS-SVM for rice leaf diseases such as Bacterial Leaf Blight (BLB), Brown spot(BS), Leaf smut(LS) and Leaf Blast using two datasets (DS1 and DS2).Accuracy of  LS-SVM is found to be 91.3% and 98.87% for DS1 and DS2 respectively whereas accuracy of SVM is 83.3% and 98.75% for DS1 and DS2 respectively. CONCLUSION: Performance of LS-SVM outperformed than SVM in terms of accuracy.