Non destructive evaluation quality of oil palm fresh fruit bunch (FFB) (Elaeis guineensis Jack) based on optical properties using artificial neural network (ANN)

Abstract

Oil palm (Elaeis guineensis Jacq) is one of the oil-producing plants with high productivity. The quality of palm fresh fruit bunches (FFB) is affected by harvesting activities. Generally, the method of harvesting oil palm by visual observation (maturity fraction) and harvest rotation systems. This study evaluated the quality of oil palm FFB non-destructively based on optical properties. Oil palm Fresh Fruit Bunches harvested in 5 ripeness ranges (110-130 DAA, 131-150 DAA, 151-170 DAA, 171-190 DAA, and 191-200 DAA). The oil palm FFB image recorded using a mobile camera with a minimum resolution of 25 megapixels, and then the image was processed using a digital image processing program. Furthermore, the quality parameters of oil palm FFB tested, and data were analyzed using SPSS Statistics 20.0 with the Artificial Neural Network (ANN) method. The model’s R2 upon calibration was 0.6934. While upon validation R2 value was 0.7211. The model was considered appropriate since R2 value both in calibration and validation were high.