Open AccessRice disease classification based on leaf image using multilevel Support Vector Machine (SVM)
Author(s) -
Dwi Ratna Sulistyaningrum,
Alima Rasyida,
Budi Setiyono
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1490/1/012053
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , kernel (algebra) , computer science , structured support vector machine , binary classification , polynomial kernel , radial basis function kernel , set (abstract data type) , machine learning , ranking svm , kernel method , mathematics , combinatorics , programming language
Plant disease is one of many factors that decrease the quality and quantity value of agriculture, especially rice plants. Automatic technology based on digital image processing is being developed to overcome this problem. Support Vector Machine (SVM) is one of the most used classifications and detection methods. SVM has been developed into multi SVM by combining several binary SVMs to classify more than two classes. In the proposed system, we use one of the multi SVM strategy, namely One Vs. All. The accuracy of classification reaches 86.10% using linear kernel. It has a higher value of accuracy than using polynomial and RBF kernel function. The scenario for the number of the dataset used is 70% for the training set and 30% for the testing set from a whole 240 images.