Stepwise discriminant analysis for colour grading of oil palm using machine vision system

The quality feature of an ordinary Elgaeis guineensis oil palm was quantified using a computer vision model in order to inspect and grade the oil palm fresh fruit bunches by an automated production system. The feature considered was colour, and the inspection criteria were based on the Palm Oil Research Institute of Malaysia. The relationship between oil contents and colour was explored in HSI (Hue, Saturation and Intensity) domain for ripeness determination. Image analysis using Wilk's Lambda and discrimination analyses were developed to inspect oil palm by four major classes: the unripe, the underripe, the ripe and the overripe. Over 400 samples were inspected from which the vision system was able to correctly classify oil palm at a greater than 90% success rate. Colour analysis was adversely affected by oil palms whose discriminant scores were located near the discrimination boundaries, and this contributed to the misclassification error. However, the misclassification rates of vision system were consistently lower compared to human inspectors, implying that the inspection system developed has a great potential to assist humans for automated oil palm grading.