Prediction of water content, sucrose and invert sugar of sugarcane using bioelectrical properties and artificial neural network

The study aimed to predict moisture content, sucrose and invert sugar of sugarcane (Saccharum officinarum L.) using artificial neural network (ANN) prediction model. The ANN model was developed based on the bioelectrical properties of the sugarcane. Bioelectrical properties were measured using LCR meter within 0.1 to 10 kHz range of frequency. The researchers then correlated the result of measurement with chemical content of sugarcane to develop an ANN prediction model. The best ANN topology (3-20-40-3) consisted of 3 nodes of input layer (inductance, capacitance and resistance), 20 nodes in hidden layer 1, 40 nodes in hidden layer 2 and 3 nodes of output layer (water content, sucrose and invert sugar) with training algorithm (trainlm), activation function of hidden layer (logsig), activation function of output layer (purelin), learning rate 0.1 and momentum 0.5. Based on the best topology, the researchers figured out that the validation of mean square error (MSE) was obtained at 0.0122. These results indicated that an ANN model based on the bioelectrical properties can be used to predict the chemical content of sugarcane