COMPARISON OF APPLE IMAGE SEGMENTATION USING BINARY CONVERSION AND K-MEANS CLUSTERING METHODS

Abstract

Apples are quite popular consumption among the community and have different kinds of shapes and colors. Apples themselves have many nutrients and various vitamins including fat, as well as energy, carbohydrates, protein, vitamin C, vitamin A, vitamin B2, vitamin B1, and many more. Because of the variety of types of apples, it is difficult for people to distinguish between these types of apples. However, with the development of technology and sophistication, it is now possible to classify the types of apples using digital images. This study aims to segment the image of apples by comparing 2 methods at once to find out which method is the best. This process is an initial stage that must be done before classifying. From the comparison results of apple image segmentation with binary conversion methods and k-means clustering, it can be concluded that the best method is k-means clustering. Because it can segment the image of apples almost perfectly.

Keywords

• image, binary conversion
• apple
• k-means clustering
• image segmentation