Fruit Classification using Neural Network Model

Authors

  • Hamirul Aini Hambali School of Computing, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
  • Sharifah Lailee Syed Abdullah Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 02600 Arau, Perlis, Malaysia.
  • Nursuriati Jamil Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selengor, Malaysia
  • Hazaruddin Harun Human-Centered Computing Research Lab, School of Computing, Universiti Utara Malaysia, Sintok, Kedah, Malaysia

Keywords:

Fruit Classification, Neural Network Model, Natural Environment,

Abstract

Fruit classification process is gaining importance in image processing applications specifically in agricultural area. However, classification process is challenging for images captured in natural environment due to the existence of nonuniform illumination. Different illuminations produce different intensity on the object surface and thus lead to inaccurate classification. Therefore, this study focuses on the improvement of development of classification model for images captured in natural environment. This study has developed a neural network (NN) model that is able to classify objects based on their surface colour. The result of the NN model shows that, with the network configuration of 6-7-4, the NN model works very well for objects exposed to the natural illumination. To justify the proof-of-concept, the proposed classification model is tested on jatropha fruit images and the results show that the developed model is able to classify the fruit accurately.

References

Y.A. Asnida,, M.I. Mohd Azlan, and I. Khudzir, “ Optimization of in-situ supercritical methanol transesterfication of jatropha curcas l. seeds using response surface methodology (RSM)”. Proceeding of 2nd International Conference on Engineering and ICT, Melaka, Malaysia, 2010.

M. Siriwardhana, G.K.C. Opathella, and M.K. Jha, “Bio-diesel:

Initiatives, potential and prospects in Thailand: A review”. Energy Policy, 37, 2009, 554-559.

E. Zulham, R. Rizauddin, A.G.Jaharah, and Y. Zahira,

“Development of jatropha curcas color grading system for ripeness evaluation”. European Journal of Scientific Research, 30(4), 2009, 662-669.

P. Sirisomboon, and P. Kitchaiya, “ Physical properties of jatropha curcas l. kernels after heat treatments”. Journal of Biosystems Engineering, 102, 2009, 244-250.

M. Fadel, L. Kurmestegy, M. Rashed, and Z. Rashed, “Fruit color properties of different cultivars of dates”. Agricultural Engineering International: the CIGR Ejournal, 8(9), 2006.

Z. Xiaobo, Z. Jiewen, and L. Yanxiao, “Apple color grading based on organization feature parameters”. Pattern Recognition Letters of Elsevier, 28, 2007, 2046-2053.

A. Haidar, H. Dong, and N. Mavridis, “Image-based date fruit classification”. Paper presented at the IV International Congress on Ultra Modern Telecommunication and Control Systems, 2012.

A/M. Nur Badariah, K. Arumugam, A. Syed Khaleel, M.S. Zainul Abidin, “Classification of fruits using probabilistic neural networks -improvement using color features”. Paper presented at he TENCON, 2011.

E. Zulham, R. Rizauddin, and A.G. Jaharah, “ A back propagation neural networks for grading jatropha curcas fruits maturity”. American Journal of Applied Sciences, 7(3), 2010, 390-394.

M.A. Abdulla, “ Smart fruit classification: Applicable for dates, King Saud University, 2007, 33.

M. Fadel, “ Date fruits classification using probabilistic neural networks”. Agricultural Engineering International: the CIGR Ejournal, 9, 2007.

M.Z. Abdullah, L.C. Guan, and B.M.N. Mohd Azemi, “Stepwise discriminant analysis for colour grading of oil palm using machine vision system”. Trans IChemE, 79, 2001, 223-231.

M.S.M. Alfatni, M.S. Abdul Rashid, M.S. Helmi Zulhaidi, O.M. Ben Saaed, and O.M. Eshanta, “ Oil palm fruit bunch grading system using red, green and blue digital number”. Journal of Applied Sciences, 8(8), 2008, 1444-1452.

S.N. Jha, S. Chopra, and A.R.P. Kingsly, “ Modeling of color values for nondestructive evaluation of maturity of mango”. Journal of Food Engineering, 78(1), 2007, 22-26.

S.P. Kang, A.R. East, and F.J. Trujillo, “Colour vision system evaluation of bicolour fruit: A case study with 'B74' mango”. Postharvest Biology and Technology, 49, 2008, 77-85.

N.S.A. Derkyi, H. Bailleres, G. Chaix, M.F. Thevenon, A.A. OtengAmoako, and S. Adu-Bredu, “Colour variation in teak tectona grandis) wood from plantations across the ecological zones of Ghana”. Ghana Journal of Forestry, 25, 2009, 40-49.

D.H. Foster, “ Color constancy”. Vision Research, 51, 2011, 674-700.

D.A. Johari, T.K.. Rahman, I. Musirin, and N. Aminuddin, “Hybrid meta-EP-ANN technique for lightning prediction under Malaysia environment”. Proceeding of 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering & Databases, 2009.

W. Chokananporn, W., and A. Tansakul, “Artificial neural network model for estimating the surface area of fresh guava”. Asian Journal of Food and Agro-Industry, 1(3), 2008, 129-136.

W. Zhang, G. Teng, and C. Wang, “ Identification of jujube trees diseases using neural network”. Optik, 124, 2013, 1034-1037.

W.M.J. Achten, L. Verchot, Y.J. Franken, E. Mathijs, V.P. Singh, R. Aerts, and B. Muys, “ Jatropha bio-diesel production and use”. Biomass and Bioenergy, 32,2008, 1063-1084.

H. Hamirul’Aini, S.A. Sharifah Lailee, J. Nursuriati, and H. Hazaruddin. “A Rule-based Segmentation Method for Fruit Images under Natural Environment”. Proceeding of the 2014 International Conference on Computer, Control, Informatics and Its Application, 2014.

F. Mendoza. “Characterization of surface appearance and color in some fruits and vegetables by image analysis”. Pontificia

Universitidad Catolica de Chile, 2005.

Y. Al Ohali, “Computer vision based date fruit grading system: Design and implementation”. Journal of King Saud University -Computer and Information Sciences, 23, 2011, 29-36.

A. Adeloye, “The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs”. Environmental Modelling & Software, 24, 2009, 1233-1240.

Downloads

Published

2017-03-01

How to Cite

Hambali, H. A., Syed Abdullah, S. L., Jamil, N., & Harun, H. (2017). Fruit Classification using Neural Network Model. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-2), 43–46. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1649

Most read articles by the same author(s)