General Information
    • ISSN: 2010-0264
    • Frequency: Bimonthly (2010-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJESD
    • Editor-in-Chief: Prof. Richard Haynes
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS), CABI, DOAJ, Ulrich Periodicals Directory, Engineering & Technology Digital Library, Electronic Journals Library, Crossref, ProQuest.
    • E-mail: ijesd@ejournal.net
  • Sep 13, 2017 News! Vol.8, No.9 has been published with online version!   [Click]
  • Jul 31, 2017 News! Vol. 8, No. 8 has been indexed by Crossref.
Editor-in-chief
The University of Queensland, Australia
It is my honor to be the editor-in-chief of IJESD. The journal publishes good papers in the field of environmental science and development.
IJESD 2012 Vol.3(2): 199-204 ISSN: 2010-0264
DOI: 10.7763/IJESD.2012.V3.215

Moisture Prediction in Maize Using Three Term Back Propagation Neural Network

Said Jadid Abdulkadir, Siti Mariyam Shamsuddin, and Roselina Sallehuddin

Abstract—The percentage of moisture content is one of the most important indexes in maize quality evaluation. Maize with high moisture content will not stay for extended periods; hence it is important to have accurate prediction of moisture content. As aflatoxin contamination in maize is of major concern, the necessity for suitable methods to predict moisture content with less time and higher accuracy assumes greater importance. Hence, Three Term Back propagation network is proposed as a prediction tool for moisture content on maize. The new model is an improvement on Two Term Back propagation by the addition of an extra parameter, the proportional factor, which increases the convergence speed and reduces learning stalls in the conventional neural network. The experimental results are conducted using semi-annual datasets obtained from a maize thermal dryer. The results show that the proposed model outperforms Two Term Back propagation and other prediction methods like empirical correlation, analytical models (tank in series) and genetic algorithm which were used as prediction tools. Quantitatively, Three Term Back Propagation Neural Network obtained a higher precision result with a Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) of 0.00145 and 0.00001 respectively.

Index Terms—Three term back propagation neural network, maize, moisture content, proportional factor.

Said J. A., Siti Mariyam S. and Roselina S. are with the Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (e-mail: jadid86@gmail.com, mariyam@utm.my, roselina@utm.my).

[PDF]

Cite: Said Jadid Abdulkadir, Siti Mariyam Shamsuddin, and Roselina Sallehuddin, "Moisture Prediction in Maize Using Three Term Back Propagation Neural Network," International Journal of Environmental Science and Development vol. 3, no. 2, pp. 199-204, 2012.

Copyright © 2008-2017. International Journal of Environmental Science and Development. All rights reserved.
E-mail: ijesd@ejournal.net