A Novel Feature Extraction Method for Identification of Healthy and Diseased Maize and Paddy Leaves Using ECOC Classifier - Naik, T. Harisha; Suresha, M; Shreekanth, K.N (International Journal of Computer Sciences and Engineering, 2018)
With the entry of huge databases and the resulting prerequisites for excellent machine learning frameworks, new issues emerge and novel feature extraction methods are in demand.Basic feature reduction methods are feature selection and feature extraction. Feature selection find the subset of those prime features in a given initial set and helps in finding optimal solution. Feature extraction method transform original set of features into new subsets which are smaller number of dimensions. Generally features contain information about the target and more features indicate more information and better discrimination power. In this paper we have proposed a novel feature extraction method for feature extraction of maize and paddy dataset. Global thresholding Otsu method is used for segmentation and Error Correcting Output Codes (ECOC) classifier is used for identification of healthy and diseased maize and paddy leaves and found a success rate of 91.32% for paddy leaves and 92.56% for maize leaves. In this experimentation the similarity difference of Gray with Cb Component has given highest accuracy for both data sets.