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Date Fruits Classification using MLP and RBF Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 10 |
| Year of Publication: 2012 |
| Authors: Khalid M. Alrajeh, Tamer. A. A. Alzohairy |
10.5120/5579-7686
|
Khalid M. Alrajeh, Tamer. A. A. Alzohairy . Date Fruits Classification using MLP and RBF Neural Networks. International Journal of Computer Applications. 41, 10 ( March 2012), 36-41. DOI=10.5120/5579-7686
Abstract
This paper presents a new date fruits sorting system using artificial neural networks (ANN). The classification system are based on attributes extracted from dates fruits obtained from a computer vision system (CVS) used. Two different models of neural networks have been applied as classifiers: multi-layer perceptron (MLP) with backpropagation and radial basis function RBF networks. The aims of this study are to define a set of external quality features from the shape and color for different types of date fruits and to examine the effectiveness of the neural network models for image classification. In the experiments for performance evaluation the neural networks achieved a recognition rate equal to 87. 5% and 91. 1% respectively for MLP with backpropagation and RBF, which is consistent with the best results reported in the literature for the same data base and testing paradigms.
References
- Baoping, J. 1999. nondestructive technology for fruits grading. International conference on Agricultural Engineering Beijing.
- Hecht-Nielson. 1989. Neurocomputing. Addison-Wesley, San Diego.
- Buzera, M. , Groza V. , Prostean G. and Prostean O. 2008. techniques of analyzing the color or produces for automatic classification. 12th IEEE International Conference On Intelligent Engineering Systems, Miami, USA, pp. 209-214.
- Cruvinel, P. E. et al. 2002. Image Processing in Automated Pattern Classification of Oranges. Transaction of the ASAE.
- Dech Sidney H. 1994. Evaluations of semi-automated vegetable sorting concept. Transaction of the ASAE.
- Laykin, S. et al. 2002. Image Processing Algorithm for Tomato Classification. Transaction of the ASAE, 45(3), 851-858.
- Meyers, J. B. 1988. Improving dynamic visual inspection performance, Unpub. M. S. thesis, Univ. of Georgia, Athens.
- Noordam, J. C. et al. 2000. High speed potato grading and quality inspection based on a color vision system, AGENG.
- Simoes A. S. et al. 2002. Appling Neural Networks to Automated Visual Fruit Sorting. Transaction of the ASAE.
- Sudhakara Rao P. Color Analysis of fruits using machine vision system for automatic sorting and grading. Journal Instrum. Soc. India. 34(4), 284-291.
- Ying Y. et al. 2003. Detecting Stem and Shape of Pears using Fourier Transformation and an Artificial Neural Network. Transaction of the ASAE. 46(1).
- Widrow and Hoff. 1960. Adaptive witching Circuits. IRE WESCON Convention Record, New York, pp. 96-104.
- Thomas, A. , Ferrari V. , Leibe B. , Tuytelaars T. and Gool L. V. 2009. Shape-from-recognition: Recognition enables meta-data transfer. Computer Vision and Image Understanding, 113(12), pp. 1222-1234.
- Sun, T. , Horng H. , Liu C. and Tien F. 2009. Invarient 2D object recognition using KRA and GRA. Expert Systems with applications. 36(9), pp. 11517-11527.
- Price, K. and Reddt R. 1979. Matching Segments of Image. IEEE Trans. On Pattern Analysis and Machine intelligence. Vol. Pami-1, no. 1, pp. 110-111.
- Nevatia, R. and Ramesh K. 1980. linear feature Extraction and description. Computer graphics and Image Processing. Vol. 13, pp. 257-269.
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