Combination of laserlight backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity Abstract   The classification of oil palm fresh fruit bunches (FFB) in relation to the maturity level is an important aspect to determine the quality and productivity of the fruit. This study evaluated the utilization of combined computer vision and laserlight backscattering imaging in determining the oil content and color changes of oil palm FFB at different maturity levels i.e. unripe, ripe, and overripe. Redgreenblue (RGB) images referring to the computer vision and backscattering images of 90 oil palm FFB samples were acquired with 30 samples per each maturity level. Standard reference methods for oil content and color values (L*, a*, and b*) were determined in relation to the quality attributes of the oil palm FFB samples. Partial least squares (PLS) and principal component analysis (PCA) were used to analyze the quality changes of oil palm FFB based on its maturity levels. Multivariate algorithms such as linear discriminant analysis and quadratic discriminant analysis were applied to evaluate the classification performance based on the combined RGB and backscattering parameters. The combined optical techniques showed a good coefficient of determination (R2) of greater than 0.80 for both oil content and color values. The average classification accuracies were also higher than 85% in classifying oil palm FFB maturity. Hence, this work has demonstrated that combined computer vision and backscattering imaging systems could be useful as a nondestructive device for evaluating the classification of oil palm FFB.

Fruit Battery Method for Oil Palm Fruit Ripeness Sensor and Comparison with Computer Vision Method Oil palm ripenessxe2x80x99 main evaluation procedure is traditionally accomplished by human vision. However, the dependency on human evaluators to grade the ripeness of oil palm fresh fruit bunches (FFBs) by traditional means could lead to inaccuracy that can cause a reduction in oil palm fruit oil extraction rate (OER). This paper emphasizes the fruit battery method to distinguish oil palm fruit FFB ripeness stages by determining the value of load resistance voltage and its moisture content resolution. In addition, computer vision using a color feature is tested on the same samples to compare the accuracy score using support vector machine (SVM). The accuracy score results of the fruit battery, computer vision, and a combination of both methodsxe2x80x99 accuracy scores are evaluated and compared. When the ripe and unripe samples were tested for load resistance voltage ranging from 10 xcexa9 to 10 kxcexa9, three resistance values were shortlisted and tested for moisture content resolution evaluation. A 1 kxcexa9 load resistance showed the best moisture content resolution, and the results were used for accuracy score evaluation comparison with computer vision. From the results obtained, the accuracy scores for the combination method are the highest, followed by the fruit battery and computer vision methods.