Automated Oil Palm Fruit Grading System using Artificial Intelligence

This project deals with the ripeness of oil palm fruit. The current procedure in the palm oil mills is grading the oil palm fruit manually using human graders. This method is subjective and inconsistence because each graders has its own techniques and may vary from each other’s. Hence, it affects the quality and quantity of the oil that can be extracted. In this project, a new model of automated grading system for oil palm fruit is developed using the RGB color model and artificial fuzzy logic. The purpose of this grading system is to distinguish between the three different classes of oil palm fruit which are underripe, ripe and overripe. The ripeness or color ripening index was based on different color intensity. The grading system uses a computer and a CCD camera to analyze and interpret images correspondent to human eye and mind. The computer program is developed for the image processing part 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 to train the data and make the classification for the oil palm fruit. The program developed has been able to distinguish the three different classes of oil palm fruit automatically with 86.67% of overall efficiency. This project provides a very good technique to standardize the oil palm fruit grading system over a large area and the research will continue to normalized the system to be able to use under different source of lighting. KeywordClassification, Grading, Color Model, Fuzzy Logic