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

Hendrawan Y., . and Niami M. W., . and Yuliatun S., . and Supriyanto S., . and Sucipto S., . and Somantri A. S., . and Al-Riza D. F., . Prediction of water content sucrose and invert sugar of sugarcane using bioelectrical properties and artificial neural network. pp. 2674-2680. ISSN 2231-7546

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.

Full text available from: Official URL: http://www.ifrj.upm.edu.my/25%20(06)%202018/(58).p...

Abstract

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.

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Item Type:	Article
AGROVOC Term:	Sugarcane
AGROVOC Term:	Water content
AGROVOC Term:	Sucrose
AGROVOC Term:	Sugar content
AGROVOC Term:	Neural networks
AGROVOC Term:	Moisture content
AGROVOC Term:	Saccharum officinarum
AGROVOC Term:	Chemical composition
AGROVOC Term:	Models
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 06:29
URI:	http://webagris.upm.edu.my/id/eprint/24969

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