Abstract

Olive fruit is a horticultural product of the oleaceae family with the genus Olea which has various types and unique features. One of a group of Olea species found in tropical and subtropical regions which make the plant fertile and abundant. The yields are very abundant in proportion to market needs. The random harvest of produce makes the selection of post-harvest products very important in classifying types of olives. So it is necessary to have a system that can classify automatically. Previous studies have been proposed to classify olives with considerable accuracy. However, the required speed takes a very long time because it uses a complex pretrained model. Therefore, this study aims to classify olives with a faster time and accuracy that is no less than before. The method to be used is Convolutional Neural Network (CNN) with its own architectural circuit. The results of this study get an accuracy of 92% with 30 epochs.