Early prediction of wheat diseases using SVM multiclass

Abstract

The early prediction of Plant diseases based on learning algorithms is one of promising research areas. Several types of classification techniques can be utilized on such data to early predict the different kinds of wheat diseases. However, the high dimension of the dataset in our case study and how selecting of the best data mining classifiers is one of the challenges. For that, Principle Component Analysis (PCA) technique was carried out for reducing the dimension by combining a set of correlated features as preprocessing step. Then, the Support Vector Machine (SVM) classifier with different multiclass techniques has been applied to predict of wheat diseases. The results have been combined with different voting methods in conjunction with PCA. The proposed system evaluated by several measurements and the classification accuracy reached to 96%.