Different Classifiers in Classification of Raw Arecanut
S Siddesha1, S K Niranjan2
1S Siddesha*, Department of Computer Applications, JSS Science and Technology University, Mysuru, India.
2S K Niranjan, Department of Computer Applications, JSS Science and Technology University, Mysuru, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1671-1676 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8414088619/2019©BEIESP | DOI: 10.35940/ijeat.F8414.088619
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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: Classification of crops is one of the important processes in precision agriculture. Classification of crops based on their verity, enhances the quality. In this paper, we presented a study of three main supervised classifiers, KNN, SVM and ANN for classifying the raw arecanut using color histogram and color moments as features. Experiments conducted over arecanut image dataset of 800 images across 4 classes. Among these classifiers K-NN gave a good result of 98.16% of with color histogram as feature.
Keywords: Color histogram, Color moments, Classification, KNN, ANN, SVM.