Novel Feature Extraction for Pineapple Ripeness Classification

Abstract

A novel feature extraction method has been proposed to improve the accuracy of the pineapple ripeness classification process. The methodology consists of six stages, namely: image acquisition, image pre-processing, color extraction, feature selection, classification and evaluation of results. The red element in the RGB model is selected as the threshold value parameter. The ripeness of pineapples is determined based on the percentage share of yellowish scales visible in images presenting the front and the back side of the fruit. The prototype system is capable of classifying pineapples into three main groups: unripe, ripe, and fully ripe. The accuracy of 86.05% has been achieved during experiments.