Extraction Analysis of Texture and Morphological Features as Characteristics of Cabbage Images

Digital images are widely used in various fields because they provide convenience and effectiveness in solving problems that exist in these fields. In its utilization, the image is extracted so that it will get characteristics or features which will then be used in the identification or classification process. Feature extraction is a decisive step in the classification or identification process using digital images. Features are unique characteristics of an object. Visually from sight, humans can distinguish objects from one another. While in the field of computer vision, feature extraction is necessary to obtain the distinguishing characteristics that distinguish one object from another. How the object in the image can represent the real object that really depends on the feature extraction that is carried out. In this study, the extraction of texture features and morphological features for cabbage objects will be carried out. Preprocessing is done before the cabbage image data that has been collected is extracted. The preprocessing stages are cropping, removing the image background, segmenting and improving image quality. The next step is the extraction of texture features using the GLCM method and morphological features by calculating the area, perimeter and others. The aim is to find out which features are able to extract the features contained in the cabbage image, so that useful information will be obtained from an image before the classification stage is carried out. Hence based on analysis of variance values and data distribution patterns, it is known that the contrast and energy of the texture features can be used as an indicator in determining the quality of cabbage. Meanwhile, the morphological features that can be used as markers are area, perimeter, major axis and minor axis.