Texture Features-Based Quantitative Analysis of Mung-Bean Varieties Using Machine Vision Approach
Abstract
This paper presents a robust and economically efficient method for the discrimination of four mung-beans varieties on the basis of quantitative parameters, which is otherwise a challenging task due to their similar physical and morphological features, such as color, shape and size etc. Digital images of the bulk samples have been used as input data are acquired in an absolute natural environment. A total number of 230 first-order and second-order Gray Level Co-Occurrence Matrices (GLCM) textural parameters are extracted from different size of regions of interest (ROIs), the most relevant 10 features are selected by Fisher’s Co-efficient and classification capability of the selected features is verified by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), whereas, a feed-forward ANN classifier has been employed for training and testing purpose. The best results are achieved with an average accuracy of 98.17% and 94.35% during training and testing respectively, when the data of 10 selected features from ROI (64*64) is deployed to the classifier.References
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[2] dawn, "Mungbean — a rich source of protein", 2007. [Online]. Available: https://www.dawn.com/news/242480
[3] J. Levy, "Mung Beans Nutrition & Its Big Benefits," 2019. [Online]. Available:https://draxe.com/mung-beans-nutrition/
[4] E. S . Oplinger, L. L. Hardman, S. Kaminski, M. Combs, and J. D. Doll, "Mungbean," University of Wisconsin-, Madison May,1990.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] D. Liu, Y. Li, 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.
[10] 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., vol. 97, no. 8 pp. 2588-2593, 2016.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] N. Panmedey, 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.
[16] 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.
[17] 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.
[18] X. Chen, Y. L. Xun, , W. U and J. Zhang, "Combining discriminant analysis and neural networks for corn variety identification," Computers and Electronics in Agriculture, vol. 71, pp. 548-553, 2010.
[19] S. Ghamari, "Classification of chickpea seeds using supervised and unsupervised neural networks," African Journal of Agricultural Research, vol. 7, pp. 3193-3201, 2012.
[20] 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.
[21] N. S. Visen, J. Paliwal, 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.
[22] 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.
[23] P. Zapotocnzy, "Application of image texture analysis for varietal classification of barly" International Agrophysics, vol. 26, pp. 81-90, 2012.
[24] A. Pourrez, H. P. Reza, 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.
[25] 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.
[26] P. M. Szczypiński, M. Strzelecki, A. Materka, and A. Klepaczko, "MaZda—A software package for image texture analysis," Computer Methods and Programs in Biomedicine, vol. 94, no. 1, pp. 66-76, 2009.
[27] S. Kara, and F. Dirgenali, "A System to Diagnose Atherosclerosis via Wavelet Transforms, Principal Component Analysis and Artificial Neural Networks" Expert Systems with Applications, vol. 32, pp. 632-640, 2007.
[28] T. Raykov, and G. A. Marcoulides, "An Introduction to Applied Multivariate Analysis. New York": Routledge Taylor & Francis Group, 2008.
[29] J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, and R. L.Tatham, "Multivariate Data Analysis, 6th ed. New Jersey": Prentice Hall, Upper Saddle River, 2006.
[30] R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification," Journal of Classification, vol. 24, no. 2 pp. 305-307, 2007.
[31] S. Haykin, "Neural Networks: A Comprehensive Foundation 2nd," Prentice Hall PTR Upper Saddle River, NJ, USA 1998.
[32] V. C. Brenda, B. E. Leticia, B. P. Maria, G. P. Roberto, and G. W. Rodolfo, "Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multielemental analysis and chemometrics tools," Food Control, vol. 242, pp. 272-278, 2018.
[33] P. R. Michael, E. G. Jose, J. H. Melisa, V. A. Maria, and G. P. Roberto, "Classification of cowpea beans using multielemental fingerprinting combined with supervised learning," Food Control, vol. 95, pp. 232-241, 2019.
