Image-based processing for ripeness classification of oil palm fruit

Abstract

Palm fruit is the result of agriculture products that processed into vegetable oil. Nowadays, there are many daily necessities are produced from palm fruit which cause demand for palm oil will increase sharply in the future. Therefore, image-based automation systems related to fruit ripeness classification continue to be developed to support the increasing result of production. In this paper, the classification method of palm fruit is aimed to distinguish three classes of fruit ripeness, namely raw, under-ripe, and ripe. The focus of this work starts from the segmentation process by applying the thresholding using the Otsu method. Following this, the color extraction features were employed by calculating two kind features, including the mean and standard deviation based on four-color components: red, green, blue, and gray, hence there are eight features produced. Lastly, classification is applied using the support vector machines method. This method was tested using160 images with the successful rate indicated by an accuracy value of 92.5%.