Artificial Neural Network Model to Predict °brix and pH of Banana Based on Color Parameters
DOI:
https://doi.org/10.23960/jtep-l.v13i3.739-749Abstract
Artificial neural network (ANN) was used to predict internal quality parameters (oBrix and pH) of lady finger banana. This research consisted of three stages, namely: (1) capturing images of lady finger banana using a computer vision system; (2) measurement of oBrix and pH of the banana; (3) ANN architecture analysis using the Matlab R2019a application. The ANN architectural model consisted of 3 output models, namely: (1) oBrix values; (2) pH value; (3) oBrix and pH values. The ANN architecture analysis was carried out through two phases. Phase I consisted of 45 experimental units and phase II with 35 experimental units. The best ANN architecture to be used as a prediction model for oBrix and pH of golden banana fruit is ANN architecture model 3 with the number of neurons inside the hidden layer = 3; activation function in hidden layer = logsig; activation function inside the output layer = logsig; data transformation range 0 − 1; learning rate value = 0.01; learning algorithm = tradingda; with MSE (mean square error), MAE (mean absolute error) performance and R correlation coefficient from training results of 0.0954; 0.2619 and 0.6538; test results 0.0392; 0.1606 and 0.7000 and validation results 0.0289; 0.1474 and 0.7889.
Keywords: Artificial neural networks, Color, Computer vision system, CVS, RGB.
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