Estimation of Oil Content in Oil Palm Fresh Fruit Bunch by Its Surface Color

Abstract

Oil palm is one of the potential tree crops in Thailand. However, the production of oil palm has been experienced many aspects. Price factor is also one of the problems. Price of oil palm depends on the amount of oil content in the oil palm fruit which are estimated by an expert. The main consideration is the ripeness of the oil palm fresh fruit bunches. An expert determines using its surface color. A different experience of experts leads to a different estimation. The problem may be solved using the chemical analysis methods which more accurate. However, it takes time and uncomfortable. In this research, artificial intelligence (AI) will be applied to estimate the oil content in a fresh fruit bunch (FFB). Two popular types of oil palms in Thailand are used in this work. The Nigrescene fruit, color varies from dark purple to red orange depending on its gene and ripeness. The Virescene fruit, color changes from green to orange. The surface color of an oil palm fruit and structure of the bunch were considered as the feature set. An oil palm FFB image from a smartphone camera was fed to the model for predicting the oil content in FFB. Several models such as multi linear regression, artificial neural network and convolution neural network will be observed. The measure of the quality’s model uses the root mean square error (RMSE). The convolution neural network produces the average of RMSE at 727 for Nigrescene and at 4.83 for Virescene.