Open AccessA comparison: SVM-Segmentation Methode To Detect Rice-Weevil In Rice Image
Author(s) -
Uvi Desi Fatmawati
Publication year - 2022
Publication title -
jatisi (jurnal teknik informatika dan sistem informasi)/jatisi: jurnal teknik informatika dan sistem informasi
Language(s) - English
Resource type - Journals
eISSN - 2503-2933
pISSN - 2407-4322
DOI - 10.35957/jatisi.v9i1.1479
Subject(s) - artificial intelligence , hsl and hsv , rice weevil , rgb color model , support vector machine , pattern recognition (psychology) , computer vision , segmentation , computer science , image segmentation , mathematics , brightness , biology , botany , sitophilus , physics , virus , virology , optics
Support Vector Machine (SVM) is a classification method that works by finding the hyperplane with the largest margin. Saturation Value (SV) is a digital image color model consisting of two elements, namely Saturation and Value. SV is taken from HSV, then only two elements are used. Segmentation is the process of separating an image that will be detected with a background image. Rice weevils are small pests that damage the quality of rice in rice storage. The quality and nutrition of rice will be reduced because of that bug. In this study, two methods have been used to detect the rice weevil that placed on a rice in an image.
In the first method, feature extraction of the rice weevil texture is taken from RGB images and feature extraction of the SV brightness values is taken from converting RGB images to HSV images. These two parameters are used as the SVM data training. In the second method, SV value in the HSV color model is used to separate between the rice weevil as the object detected and a rice as the background. The results showed that the first method provides an accuracy rate of 78.95% while the second method is 84.78%.
Keywords—SVM, HSV, Segmentation, Rice Weevils, Accuracy