Paddy Seed Classification and Identifying Varieties using Random Assessment Classification

Abstract

The current research work focuses in developing an accurate and efficient classification and feature extraction algorithm for paddy seed image analysis. The paddy images that are preprocessed by applying hybrid mediangaustransform algorithms were segmented using Paddysegmatch algorithm. The resultant image’s features are extracted by applying the proposed enhanced rapid SURF feature extraction including various features of image. Later, the paddy seeds are classified to form different categories by applying the proposed Random Assessment Classification algorithm. Experimental results on Paddy seed realtime image analysis database show that the proposed method performs better classification accuracy compared with SVM and KNN classification algorithms.