Potential Application of Color and Hyperspectral Images for Estimation of Weight and Ripeness of Oil Palm (Elaeis guineensis Jacq. var. tenera)

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 (R2) 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.