Open AccessPaddy Seed Classification and Identifying Varieties using Random Assessment Classification
Author(s) -
S. Maheswari,
M. Renuga Devi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9879.129219
Subject(s) - support vector machine , pattern recognition (psychology) , random forest , artificial intelligence , feature extraction , computer science , contextual image classification , feature (linguistics) , statistical classification , image (mathematics) , data mining , philosophy , linguistics
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.