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.

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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

Issue

Vol. 9 No. 1-2: Advancing Digital Societies for Social and Cultural Improvement

Section

Articles

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