Paddy Seed Classification and Identifying Varieties using Random Assessment Classification
S. Maheswari1, M. Renuga Devi2
1Mrs. S. Maheswari, Asst. Prof., Department of B.Sc Computer Science at Vidyasagar College of Arts and Science and NAAC Accrediated Institution, Udumalpet, Tripur (Dt), Tamil Nadu, India.
2Dr. M. Renuka Devi, Professor & Head, Department of BCA, Sri Krishna Arts and Science College, Coimbatore (Dt), Tamilnadu. India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2682-2685 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A9879109119/2019©BEIESP | DOI: 10.35940/ijeat.A9879.129219
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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: 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.
Keywords: Feature Extraction, Classification, SURF, Random Assessment Classification.