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This work presents a method to predict the lycopene quantity in the gac-fruit using coupled RGB images analysis and artificial neural network (ANN). 75 gac-fruits were photographed and the photos were analyzed to separate the RGB extraction, later the data were used in the prediction model. Each gac-fruit was tested to search for lycopene using UV-spectrophotometric and was used as the output data for the training and testing process of the model. The 3 layers ANN models with different numbers of hidden node (2-12), epoch number (300-1,500), learning rate (0.1-0.4) and different momentum (0.1-0.4) were developed and examined. In addition, the k-nearest neighbor (KNN) with different k parameters (2-10) was developed to compare with prediction from the ANN model. The best prediction results are obtained from the ANN model with the number of nodes in the input layer, hidden layer and output layer are 3, 3 and 1 respectively. The results showed that the mean squared error (MSE) was 1.7252 and the regression coefficient of determination (R2) was 0.9867.
Keywords: gac-fruit; lycopene; artificial neural network; image processing; k-nearest neighbor
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