Sorting of pistachio nuts using image processing techniques and an adaptive neural-fuzzy inference system

ماشین های کشاورزی

Article 6, Volume 6, Issue 1 - Serial Number 11, 2016, Pages 60-68   

Document Type: Research Article

DOI: 10.22067/jam.v6i1.33097

Authors

A. R. Abdollahnejad Barough^{* 1}; M. Adelinia¹; M. Mohamadi²

¹Technical and Vocational University of Shahid Shamsipour, Computer Institute, Tehran, Iran

²Shahid Beheshti University, School of Electrical and Computer Engineering, Tehran, Iran

Abstract

Introduction: Pistachio nut is one of the most important agricultural products of Iran and it is priced due to the quality and type. One of the significant factors of pistachio cost is its type in terms of shell. Filled split pistachio nut has the most quality and is utilized as nuts, while the closed shell type has lower cost, at the same time is economically efficient in food industry such as confectionery. Now, pistachio sorting is performed usually by human and sometimes using electromechanical apparatuses. Classification of pistachio by human is time consuming and is done with an unacceptable accuracy, on the other hand, electromechanical and electro optical apparatuses damages pistachio because the mechanism used in them while separating. So, the need to develop automated systems that could be implemented by intelligent ways is evident to increase the speed and accuracy of classification.
Materials and Methods: In this study, 300 samples of pistachios contains 100 Filled split, 100 Filled non-split and 100 split blank nuts ones are used. The training set consisted of 60 samples of each type of opened nuts, closed and empty opened shell nuts a total of 180 samples and the evaluation set consisted of 40 samples of each type of opened shell, closed shell and empty opened shell nuts a total of 120 samples.
The principle of this study is implemented in two steps: 1) sample imaging and image processing to extract features 2) fuzzy network design based on the characteristics of data and training.
To select useful features from the hypothesis, C4.5 decision tree is used. C4.5 algorithm makes a greedy top to bottom search on the hypothesis, and is made by the question what feature must be at the root of the tree. By the help of statistical methods, extracted features from the images were prioritized and the most appropriate features for classification of training set were selected. The algorithm chooses the best features as their number is minimum. Finally, a total amount of the second moment (m2) and matrix vectors of image were selected as features. Features and rules produced from decision tree fed into an Adaptable Neuro-fuzzy Inference System (ANFIS). ANFIS provides a neural network based on Fuzzy Inference System (FIS) can produce appropriate output corresponding input patterns.
Results and Discussion: The proposed model was trained and tested inside ANFIS Editor of the MATLAB software. 300 images, including closed shell, pithy and empty pistachio were selected for training and testing. This network uses 200 data related to these two features and were trained over 200 courses, the accuracy of the result was 95.8%. 100 image have been used to test network over 40 courses with accuracy 97%. The time for the training and testing steps are 0.73 and 0.31 seconds, respectively, and the time to choose the features and rules was 2.1 seconds.
Conclusions: In this study, a model was introduced to sort non- split nuts, blank nuts and filled nuts pistachios. Evaluation of training and testing, shows that the model has the ability to classify different types of nuts with high precision. In the previously proposed methods, merely non-split and split pistachio nuts were sorted and being filled or blank nuts is unrecognizable. Nevertheless, accuracy of the mentioned method is 95.56 percent. As well as, other method sorted non-split and split pistachio nuts with an accuracy of 98% and 85% respectively for training and testing steps. The model proposed in this study is better than the other methods and it is encouraging for the improvement and development of the model.

Keywords

Algorithm C4.5; Image processing; Neuro-fuzzy system; Pistachios classification

References

1. Abdollahnejad Barough A. R., M. Mohamadi, and Y. Abdollahzadeh. 2013. Design of an expert system for classification of pistachio nuts using microwave, Neural Networks and image processing techniques, International Conference on Computer, Information Technology and Digital Media (CITaDiM). Iran, Tehran. 25-29.

2. Abdollahnejad Barough, A. R. 2013. Pistachiosorting using image processing techniques, microwave and neural net works, Iranian Conference on Machine Vision and Image Processing, Eighth Conference, Zanjan University, 330-334.

3. Cetin, A. E., T. C. Pearson, and A. H. Tewfik. 2004. Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics, Proc. Of IEEE Conf. Acoustics, Speech, and Signal Processing.

4. Ghazanfari, A., J. Irudayaraj, A. Kusalik, and M. Romaniuk. 1997. Machine vision grading of pistachio nuts using furrier descriptors. Journal of Agricultural Engineering Research 247-252.

5. Ghezelbash, J., A. M. Borghaee1, S. Minaei, S. Fazli, and M. Moradi. 2013. Design and implementation of a low cost computer vision system for sorting of closed-shell pistachio nuts. 8 (49): 6479-6484.

6. Gonzalez Rafael, C., E. Woods Richard, and L. Eddins Steven. 2009, Digital Image Processing Using MATLAB 2nd Ed, Gatesmark Publishing, ISBN 978-0-9820854-0-0.

7. Haff, R. P., and S. Jackson .2012. Low Cost Real-Time Sorting of In-Shell Pistachio Nuts from Kernels, Applied Industrial Optics: Spectroscopy, Imaging and Metrology Monterey, California United States. ISBN: 1-55752-947-7, Optical Systems for the Food Industry (ATu3A).

8. Haff, R. P., T. C. Pearson, and N. Toyofuku. 2013. Sorting of In-Shell Pistachio Nuts from Kernels Using Color Imaging.

9. Haff, R. P., and T. C. Pearson. 2007. Separating in-shell pistachio nuts from kernels using impact vibration analysis, published by Springer Sens. & Instrument. Food Qual. 1: 188-192.

10. Jalali, A., and A. Mahmoudi. 2013. Pistachio nut varieties sorting by data mining and fuzzy logic classifier, International Journal of Agriculture and Crop Sciences, IJACS/2013/5-2/101-108, ISSN 2227-670X.

11. Jang, J., and R. Shing. 1997. Neuro-Fuzzy and Soft Computing, Prentice-Hill.

12. Kouchakzadeh, A., and A. Brati. 2012. Discrimination of Pistachios Varieties with Neural Network using some Physical Characteristic, Int. J. Emerg. Sci. 2 (2): 259-267.

13. Mahmoudi, A., M. Omid, A. Aghagolzadeh, and A. M. Borgayee. 2006. Grading of Iranian’s export pistachio nuts based on artificial neural networks. International Journal of Agriculture & Biology 1560 /8530: 371-376.

14. Mitchel, T. M. 1997. Machine Learning, McGraw-Hill.

15. Omid, M. 2010. Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier, Elsevier, Expert Systems with Applications.

16. Omid, M., A. Mahmoudi, and M. H. Omid. 2009. An intelligent system for sorting pistachio nut varieties. Expert Systems with Applications 36 (9): 11528-11535.

17. Pearson, T. C. 2001. Detection of pistachio nuts with closed shells using impact acoustics, Applied Engineering in Agriculture 17: 249-253.

18. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning, Morgan Kaufman Publisher, ISBN: 978-1-55860-238-0.