Variance Analysis Based Mango Recognition Using Correlation Distance

Автор: Farhana Tazmim Pinki, S.M. Mohidul Islam

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 5 vol.12, 2020 года.

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Mango plays a major role in the Agro industry and it is a very popular fruit to most of the people due to its flavor and taste. There are many varieties of mangoes that are differentiable based on their various characteristics. Sometimes it is difficult and time consuming for general people or farmers to categorize the mango into different types due to intra-class variation among various types of mangoes. This paper has proposed an automatic system to recognize mangoes thus it becomes convenient to identify various types of mangoes. In this method, mangoes are recognized into different categories based on variance analysis or data dispersion measures. Measures include five number summary, variance, mean deviation, skewness, coefficient of variation which are used as features. From both training and query images, feature vectors are created. Correlation is used to recognize mangoes into various categories. The proposed method shows better result than some existing methods.

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Image Processing, Variance, Correlation, Feature Vector, Mango Recognition

Короткий адрес: https://sciup.org/15017369

IDR: 15017369   |   DOI: 10.5815/ijigsp.2020.05.04

Список литературы Variance Analysis Based Mango Recognition Using Correlation Distance

  • The Top Mango Producing Countries in the World, https://www.worldatlas.com/articles/the-top-mango-producing-countries-in-the-world.html, Accessed on 9 April 2019.
  • Rohan Sriram, Amar Tejas M, Prof. J. Girija, “Mango Classification using Convolutional Neural Networks”, International Research Journal of Engineering and Technology (IRJET), Vol. 5, Issue. 11, 2018.
  • Behera, Santi Kumari, et al., “Automatic Classification of Mango Using Statistical Feature and SVM.” Advances in Computer, Communication and Control. Springer, Singapore, pp. 469-475, 2019.
  • Sahu, Dameshwari, and Ravindra Manohar Potdar, “Defect identification and maturity detection of mango fruits using image analysis.”, American Journal of Artificial Intelligence. Vol. 1, No. 1, pp. 5-14, 2017.
  • R Anurekha, D., and R. A. Sankaran, “Efficient classification and grading of MANGOES with GANFIS for improved performance.”, Multimedia Tools and Applications, pp. 1-16, 2019.
  • Roomi, S. Mohamed Mansoor, et al., “Classification of mangoes by object features and contour modeling.” 2012 International Conference on Machine Vision and Image Processing (MVIP). IEEE, 2012.
  • Abbas, Q., Iqbal, M. M., Niazi, S., Noureen, M., Ahmad, M. S., Nisa, M., & Malik, M. K.: Mango Classification Using Texture & Shape Features. International Journal of Computer Science and Network Security, 18(8), pp. 132-138, 2018.
  • MaZda Web Site. In: Eletel.p.lodz.pl, http://www.eletel.p.lodz.pl/programy/mazda/index.php?action=mazda, Accessed on 29 June 2019.
  • Pandey, Rashmi, Nikunj Gamit, and Sapan Naik, “A novel non-destructive grading method for Mango (Mangifera Indica L.) using fuzzy expert system.”, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014.
  • Mishra, Alok, Pallavi Asthana, and Pooja Khanna., “The quality identification of fruits in image processing using Matlab.”, International Journal of Research in Engineering and Technology, Vol. 3, No. 10, pp. 92-95, 2014.
  • Han, J., Pei, J., & Kamber, M.: Data Mining Concepts and Techniques. 3rd edn. Elsevier, 2011.
  • Correlation - Overview, Formula, and Practical Example, https://corporatefinanceinstitute.com/resources/knowledge/finance/correlation/, Accessed on 5 July 2019.
  • Mango Dataset - Studio Setup, https://data.mendeley.com/datasets/fmfncxjz3v/1, Ac-cessed on 27 March 2019.
  • Confusion Matrix in Machine Learning - GeeksforGeeks. In: GeeksforGeeks. https://www.geeksforgeeks.org/confusion-matrix-machine-learning/, Accessed on 9 June 2019.
  • Singla, A., & Garg, M.: CBIR approach based on combined HSV auto correlogram color moments and Gabor wavelet. International Journal of Engineering and Computer Science, 3(10), pp. 9007-9012, 2014.
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