International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Implementation of ML Algorithm for Mung Bean Classification using Smart Phone

  • Almutairi, Mubarak (College of Computer Science and Engineering, University of Hafr Albatin) ;
  • Mutiullah, Mutiullah (Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology) ;
  • Munir, Kashif (Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology) ;
  • Hashmi, Shadab Alam (Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology)
  • 투고 : 2021.11.05
  • 발행 : 2021.11.30
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초록

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

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과제정보

We acknowledge the co-operation of Agriculture Regional Research Centre, Bahawalpur Region, Bahawalpur. Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan, provides a pleasant atmosphere and resources to complete this work.

참고문헌

  1. P. K. Dahiya, A. R. Linnemann, M. A. J. S. Van Boekel, N. Khetarpaul, R. B. Grewal, and M. J. R. Nout, "Mung Bean: Technological and Nutritional Potential," Crit. Rev. Food Sci. Nutr., vol. 55, no. 5, pp. 670-688, 2015. https://doi.org/10.1080/10408398.2012.671202
  2. P. K. Pakistan. (2015, 12-06-2016). Mungbean crop pulse in Pakistan. Available: http://edu.par.com.pk/wiki/pluses/mung-bean/
  3. Z. Anwer. (2007, 10-06-2016). Mungbean -- a rich source of protin. Available: PAKISSAN.com
  4. D. Axe. (2016, 20-06-2016). Mung Beans Nutrition & Its Big Benefits. Available: https://draxe.com/mung-beans-nutrition/
  5. E. S. Oplinger, L. L. Hardman, A. R. Kaminski, S. M. Combs, and J. D. Doll, "Mungbean," University of Wisconsin-, MadisonMay,1990.
  6. Mahajan, Shveta, Das, Amitava, Sardana, and H. Kumar, "Image acquisition techniques for assessment of legume quality," Trends in Food Science & Technology, vol. 42, pp. 116-133, 2015. https://doi.org/10.1016/j.tifs.2015.01.001
  7. M. A. S. a. S. J. Symons, "A machine vision system for grading lentils," Canadian Grain Commission vol. 43, pp. 7.7-7.14, 2001 2001.
  8. B. S. a. D. G. S. Anami, "Improved Method for identification and classification of foreign boundries mixed food grain image sample," ICGST- International Journal on Artificial Intelligence and Machine Learning, vol. 9, pp. 1-8, 2009 2009.
  9. F. Kurtulmus, I. Alibas, and I. Kavdir, "Classification of pepper seeds using machine vision based on neural network " Int. J. Agric. and Biol. Eng. , vol. 9, pp. 51-62, 2016.
  10. D. Li, Y. Liu, and L. Goa, "Research of maize seeds classification recognition based on the image processing," International Journal of Signal Processing, Image Processing and Pattern Recognition vol. 9, pp. 181-190, 2016.
  11. K. Sabanci, A. Kayabasi, and A. Toktas, "Computer vision based-method for classification of wheat grains using artificial neural networks " Journal of Science Food Agric., 2016.
  12. P. Zapotocnzy, "Discrimination of wheat grain varieties using image analysis and multidimensional analysis texture of grain mass " International Journal of food properties, vol. 17, pp. 139-151, 2014. https://doi.org/10.1080/10942912.2011.615085
  13. M. Huang, J. Tang, B. Yang, and Q. Zhu, "Classification of maize seeds of different years baased on hyperspectral imaging and model updating," Computers and Electronics in Agriculture vol. 122, pp. 139-145, 2016. https://doi.org/10.1016/j.compag.2016.01.029
  14. X. Zhang, F. Liu, Y. He, and X. Li, "Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds " Sensors, vol. 12, pp. 17234-17246, 2012. https://doi.org/10.3390/s121217234
  15. A. A. Abdullah and M. A. Quteishat, "Wheat seeds classification using multi-layer perceptron artificial neural network " International Journal of Electronics Communication and Computer Engineering vol. 6, pp. 307-309, 2015.
  16. N. Pandey, S. Krishna, and S. Sharma, "Automatic seed classification by shape and color features by using machine vision technology," Internatoinal Journal of Computer Applications Technology and Research vol. 2, pp. 208-213, 2013. https://doi.org/10.7753/IJCATR0202.1023
  17. Neelam and J. Gupta, "Identification and Classification of Rice varieties using Mahalanobis Distance by Computer Vsision " Journal of Scientific and Research Publications vol. 5, pp. 1-5, 2016.
  18. R. Birla and P. A. Singh, "An efficient method for quality analysis of rice using machine vision system " Journal of Advance Information Technology vol. 6, pp. 140-145, 2015.
  19. X. Chen, Y. Xun, W. Li, and J. Zhang, "Combining discriminant analysis and neural networks for corn variety identification " Computers and Electronics in Agriculture, vol. 71, pp. 548-553, 2010.
  20. S. Ghamari, "Classification of chickpea seeds using supervised and unsupervised neural networks," African Journal of Agricultural Research vol. 7, pp. 3193-3201, 2012.
  21. H. K. Mebatsion, J. Paliwal, and D. S. Jayas, "Automatic classification of non-touching cereal grains in digital images using limited morphological and color features," Computers and Electronics in Agriculture, vol. 90, pp. 99- 105, 2013. https://doi.org/10.1016/j.compag.2012.09.007
  22. N. S. Visen, J. Paliwal, J. D.S., and N. D. G. White, "Image analysis of bulk grain samples using neural networks," Canadian Biosystems Engineering, vol. 46, pp. 7.11-7.15, 2004.
  23. M. Shahid, M. S. Naweed, E. A. Rehmani, and Mutiullah, "Varietal discrimination of wheat seeds by machine vision approach " Life Science Journal vol. 11, pp. 245-252, 2014.
  24. P. Zapotocnzy, "Application of image texture analysis for varietal classification of barly " International Agrophysics, vol. 26, pp. 81-90, 2012. https://doi.org/10.2478/v10247-012-0012-z
  25. A. Pourreza, H. Pourreza, M. Abbaspur-Fard, and H. Sadrina, "Identification of nine iranian wheat varieties by texture analysis," Computers and Electronics in Agriculture, vol. 83, pp. 102-108, 2012. https://doi.org/10.1016/j.compag.2012.02.005
  26. B. V. Canizo, L. B. Escudero, M. B. Perez, R. G. Pellerano, and R. G. Wuilloud, "Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools," Food chemistry, vol. 242, pp. 272-278, 2018. https://doi.org/10.1016/j.foodchem.2017.09.062
  27. M. Perez-Rodriguez, J. E. Gaiad, M. J. Hidalgo, M. V. Avanza, and R. G. Pellerano, "Classification of cowpea beans using multielemental fingerprinting combined with supervised learning," Food Control, vol. 95, pp. 232-241, 2019. https://doi.org/10.1016/j.foodcont.2018.08.001
  28. A. Materka and P. Szypinski, "MaZda User's Manual MaZda 4.6. download link; 1999-2006," ed, 2009.
  29. R. M. Haralick, K. Shanmugam, and I. Distein, "Texture Features for Image Classification," IEEE Transactions on System, Man and Cybernetics, vol. SMC-3, pp. 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314