The goal of this study is to develop an approach to determine the internal qualities in oil palm (Elaeis guineensis Jacq. var. tenera). Bunches and fruits belonging to 4 classes of ripeness (overripe, ripe, underripe and unripe) were used for this study. For these bunches, three of internal qualities as ripeness, oil content and free fatty acid content were examined. Since the estimation of internal qualities based on the overall data for a bunch was difficult, we focused on the average reflectance and the average relative reflectance values of fruits that were not concealed by fronds in bunch. By our approach, it was necessary to estimate the ripeness of the bunch before the oil content and free fatty acid content were determined. To classify ripeness of a bunch, the average relative reflectance values of bunches in different classes of ripeness were used and classified based on Euclidean distance. In addition, ratio of chlorophyll to carotenoids (R p ) was also used for estimating ripeness of a bunch. Then oil content (OC) and free fatty acid (FFA) content were predicted by calibration models corresponding to the class of ripeness. Correct estimation results in all classes of ripeness were obtained by both methods. The coefficients of determination (R 2 ) were 99.7% and 99.5% with a standard error of prediction (SEP) of 0.421 and 0.190 in the validation of oil content and free fatty acid models, respectively. For oil palm fruits, methods to estimate the ripeness of the fruits were developed. Ripeness estimation using the average relative reflectance values in lower part of the fruit was compared with ripeness estimation using the ratio of a not-pale greenish yellow area, a not-yellow area and a not-reddish orange area to the entire area of fruit. The correct estimation in all classes of ripeness was obtained by using the average relative reflectance at lower part of fruit while a correct ripeness estimation rate of 97.92% was gained by using ratio of area in fruit. Since the ripeness estimation using the ratio of the area of the fruits can be done automatically, it may provide more practically applicable for the assessment of fruit ripeness in the factory.

The intent of this study was to develop a technique for weight and ripeness estimation of oil palm (Elaeis guieensis Jacq. var. tenera) bunches from hyperspectral and RGB color images. In the experiments, color and hyperspectral images of the bunch were acquired from four different angles, each differing by 90 degrees. Acquired RGB color images were converted to HSI and L*a*b color space. Gray-scale thresholds were used to identify the area of the bunch and the area of space between the fruits. The total number of pixels in the bunch and the space were counted, respectively. In the hyperspectral images, the total number of pixels in the bunch was also counted from an image composed of three wavelengths (560 nm, 680 nm, and 740 nm), while the total number of pixels of space between fruits was obtained at a wavelength of 910 nm. From these sets of data, weight-estimation equations were determined by linear regression (LR) or multiple linear regression (MLR). As a result, the coefficient of determination (R 2 ) of actual weight and estimated weight were at a level of 0.989 and 0.992 for color and hyperspectral images, respectively. Estimation of oil palm bunch ripeness was also tested. Bunches belonging to 4 classes of ripeness (overripe, ripe, underripe, and unripe) were used for this study. Since ripeness estimation from overall data from a bunch was quite difficult, we focused on the difference in colors or reflectivity of the portion concealed and not-concealed with fronds. Euclidean distances between the test sample and the standard 4 classes of ripeness were calculated, and the test sample was classified into the ripeness class that had the shortest distance from the sample. In the classification based on color image, average RGB values of concealed and not-concealed areas were used, while in hyperspectral images the average intensity values of fruits pixels from the concealed area were used. The results of validation experiments with the developed estimation methods indicated acceptable estimation accuracy, and a possibility for practical use to estimate the ripeness of oil palm bunches.