Detection of palm fruit maturity level in the grading process through image recognition and fuzzy inference system to improve quality and productivity of crude palm oil (CPO)

Abstract

The agricultural sub-sector that has the potential to develop is the plantation sub-sector. The plantation sub-sector contributes around 3.57% in GDP. Plantation sub-sector commodities whose growth is very rapid are oil palm which is the basic ingredient in making palm oil (CPO). The world need for oil and vegetable fats which continues to increase, must be supported by an increase in palm oil production. Determination / grading of oil palm fruit that is still done manually is certainly not effective if done for the large-scale oil palm industry, so there needs to be innovation in the process of selecting / grading palm fruit to obtain the right maturity of palm fruit. This research uses research & development methods. In general, this research detects the maturity level of palm fruit from its outer color. Test the colour of the outer palm fruit based on image taking. Parameters were formed based on the matrix and used the image process that is obtained as input values on the fuzzy inference system. Hardware integration with a digital image processing computing system is done so that the resulting data can be directly processed and recognized in real time. The recognition system for oil palm fruit maturity can be done by: sample data fulfillment, pre-processing of sample images, extraction of image data using the GLCM method, determining extraction features using regression methods, dividing data into training data and test data, doing clustering, building fuzzy systems use training data, testing the accuracy of the system. Obtained accuracy for training data is 73.07% and testing data is 71.4%.