Automated ripeness assessment of oil palm fruit using RGB and fuzzy logic technique

Abstract

An objective and accurate ripeness assessment of oil palm fruit (Elaeis guineensis) is important in ensuring optimum yield of high quality oil. The ability to correctly identifying physiological maturity and harvest maturity of oil palm fruits will ensure timely harvest to avoid cutting of either under- and over-ripe fruits. This paper deals with the development of an automated ripeness assessment of oil palm fruit using RGB color model and fuzzy logic technique. The developed technique can overcome the subjectivity and inconsistency of the currently manual human grading technique based on experience. The three different classes of oil palm fruit considered are under ripe, ripe and overripe. The ripeness of the fruit is based on different color intensity. The grading system uses a computer, a CCD camera and MATLAB software to analyze and interpret images correspondent to human eye and mind. The computer program is developed for the image processing parts like the segmentation of colors, the calculation of the mean color intensity based on RGB color model and the decision making process using fuzzy logic where the data is trained and the classification is made for the oil palm fruit. The simulation results demonstrate the ability in distinguishing the three different classes of oil palm fruit automatically with overall efficiency of 88.74%. The ripeness assessment technique proposed in this paper proved a promising result and capable to be applied to improve and standardize the current oil palm fruit grading system.