Ojo Adedayo1*, Moses Onibonoje1, Maryam Isa2

1 Department of Electrical Electronic and Computer Engineering, College of Engineering, Afe Babalola University Ado Ekiti, Ekiti State, Nigeria
2 Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang Selangor, Malaysia

Download Citation: |
Download PDF


ABSTRACT

This paper presents an intelligent means of addressing characterization and grading problems in the oil palm industry for the purpose of quality control. A Layer-Sensitivity Based Artificial Neural Network (LSB_ANN) which updates its layer weights based on sensitivity analysis was designed to predict the oil content and dielectric constant of mature oil palm fruitlets. The LSB_ANN was designed, optimized and trained with 604 data points obtained from laboratory microwave coaxial sensor measurements within 2-4 GHz. The performance evaluation of the model when tested with a separate set of data showed that the properties of the fruitlets were accurately modeled. To further investigate the generalization ability of the trained neural network, three other neural network training algorithms were deployed for the same dataset. A multi-criteria evaluation of the performances of the networks showed that the proposed LSB_ANN outperformed the other three in generalization accuracy, time and computing resources. The LSB_ANN therefore represents a handy tool for rapid and intelligent characterization of oil palm fruitlets for quality control and research purposes.


Keywords: Open-ended coaxial sensor, Sensitivity analysis, Artificial neural network, Training algorithms, Dielectric properties, Oil palm fruitlets.


Share this article with your colleagues

 


REFERENCES

  1. Abbas, Z., Shaari, A.H., Khalid, K., Hassan, J., Saion, E. 2005. Complex permittivity and moisture measurements of oil palm fruits using an open-ended coaxial sensor. IEEE Sens. J., 5, 1281–1287.

  2. Blackham, D.V., Pollard, R.D. 1997. An improved technique for perimittivity measurements using a coaxial probe. IEEE Trans. Instrum. Meas., 46, 1093–1099.

  3. Castillo, E., Guijarro-berdi, B. 2006. A very fast learning method for neural networks based on sensitivity analysis. J. Mach. Learn. Res., 7, 1159–1182.

  4. Castro-Giráldez, M., Fito, P.J., Chenoll, C., Fito, P. 2010. Development of a dielectric spectroscopy technique for the determination of apple (Granny Smith) maturity. Innov. Food Sci. Emerg. Technol., 11, 749–754.

  5. Erzin, Y., Rao, B.H., Patel, A., Gumaste, S.D., Singh, D.N. 2010. Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity Int. J. Therm. Sci., 49, 118–130.

  6. Erzin, Y., Rao, B.H., Singh, D.N. 2008. Artificial neural network models for predicting soil thermal resistivity.  Int. J. Therm. Sci., 47, 1347–1358.

  7. Ghaffari, A., Abdollahi, H., Khoshayand, M.R., Bozchalooi, I.S., Dadgar, A., Rafiee-Tehrani, M. 2006. Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int. J. Pharm., 327, 126–38.

  8. Ismail, F.S., Khalid, N.E.A., Bakar, N.A., Mamat, R. 2011. Optimizing oil palm fiberboard properties using neural network. Conf. Data Min. Optim., 28–29.

  9. Negnevitsky, M. 2005. Artificial intelligence: A guide to intelligent systems, second edi. Pearson Education Limited.

  10. Ong, H.C., Mahlia, T.M.I., Masjuki, H.H., Norhasyima, R.S. 2011. Comparison of palm oil, Jatropha curcas and Calophyllum inophyllum for biodiesel: A review, Renew. Sustain. Energy Rev., 15, 3501–3515.

  11. Otkovic, I.I. 2013. Calibration of microsimulation traffic model using neural network approach. Expert Systems with Applications, 40, 5965–5974.

  12. Rhodes, M. 2013. Underwater electromagnetic propagation: Re-evaluating wireless capabilities.  http://www.hydro-international.com.

  13. Rodger, J.A. 2014. A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Systems with Applications. 41, 1813–1829.

  14. Sheela, K.G., Deepa, S.N. 2013. Neurocomputing neural network based hybrid computing model for wind speed prediction. Neurocomputing, 122, 425–429.

  15. Trabelsi, S., Nelson, S.O. 2006. Nondestructive sensing of bulk density and moisture content in shelled peanuts from microwave permittivity measurements. Food Control, 17, 304–311.

  16. Wilamowski, B.M., Yu, H. 2010. Improved computation for Levenberg-Marquardt training. IEEE Trans. neural networks, 21, 930–7.

  17. You, K.Y., Salleh, J., Abd Malek, M.F., Abbas, Z., Ee Meng, C., Yee, L.K. 2012. Modeling of coaxial slot waveguides using analytical and numerical approaches: Revisited.  Int. J. Antennas Propag. 2012, 1–12.


ARTICLE INFORMATION

Received: 2019-02-26

Accepted: 2020-12-24
Available Online: 2021-03-01


Cite this article:

Ojo A., Onibonoje, M., Isa, M. 2021. A layer-sensitivity based artificial neural network for characterization of oil palm fruitlets. International Journal of Applied Science and Engineering, 18, 2019013. https://doi.org/10.6703/IJASE.202103_18(1).011

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Other people also read ...