Open AccessDifferent Classifiers in Classification of Raw Arecanut
Author(s) -
S Siddesha,
S. K. Niranjan
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.f8414.088619
Subject(s) - pattern recognition (psychology) , artificial intelligence , histogram , support vector machine , feature (linguistics) , mathematics , computer science , image (mathematics) , philosophy , linguistics
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.