Palm oil classification using RGB and fuzzy - Khairun Nor Aimi, Ghazali (2010)
This research is about to classification palm oil by using RGB and Fuzzy. As we know the current practice in the oil palm is to grade the oil palm bunches manually using human graders for separate which one for producing oil or others effectiveness. This method is subjective and subject to disputes. In this case, we developed systems by using image processing technique RGB as a preprocessing and fuzzy logic as classifier. The RGB color technique is utilized as the extracted features for the oil palm fruit rind. Further, the extracted feature is classified using fuzzy logic system to determine the ripeness level of the oil palm fruit. The system has been design to act like human eye and brain by process the fruit images and made a decision based on selected category. The result shows that it was successful discriminate the fruit bunches with accuracy False Rejection Rate (FRR) about 10% and False Acceptance Rate 0% for ripe categories, 20% and 0% False Rejection Rate (FRR) and 0% for False Acceptance Rate (FAR) for under ripe and unripe categories.