An Efficient Rice Variety Identification Scheme Using Shape, Harlick & Color Feature Extraction and Multiclass SVM
M.Senthil Kumar1, Md.Javeed2
1M.Senthil Kumar, Professor, Electronics and Communication Engineering, Sree Dattha Institute of Engineering & Science, Hyderabad, Telangana, India.
2Md. Javeed, Electronics and Communication Engineering, Sree Dattha Institute of Engineering & Science, Hyderabad, Telangana, India.
Manuscript received on February 05, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 3629-3632 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9362088619/19©BEIESP | DOI: 10.35940/ijeat.F9362.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Rice is primary food harvests that each and every one person eats in throughout the globe, particularly in Asian nation. It is mostly classified in relation to its texture, color, grain shape etc. In this work, machine vision system is used for rice classification in order to distinguish rice varieties by using some special features like color, harlick and shape. Initially, real rice images are taken from camera for variety of rice such as Basmati rice, IR 18, Ponni, Ponni Leader and Ration rice. These images are taken as input image. Then special preprocessing schemes are introduced like Image thresholding, image enhancement, sharpening and filtering are used to analyze the rice variety. After that feature extraction processes are carried out for both training and testing images. Finally, the multiclass support vector machine (M-SVM) is incorporated to identify the rice variety based on matching between the feature values of training and testing images. These rice classification results such as accuracy and complexity are compared with all other existing classification processes.
Keywords: Rice classification; Harlick feature; shape fature; color feature; multiclass SVM; accuracy.