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IJSTR >> Volume 9 - Issue 8, August 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Statistical Analysis Of The Features And Classification Of Coffee Beans In Three Maturation Stages

[Full Text]

 

AUTHOR(S)

Jose Alfredo Palacio-Fernández, William Orozco, Bayardo Cadavid

 

KEYWORDS

features, classifier, main components, coffee, wavelet, image

 

ABSTRACT

This article presents a statistical analysis of the features of RGB, HSV, Wavelet and the relation of coffee axes based on the root square mean value, the standard deviation and the Wavelet approximation coefficients’ average for the images obtained from three types of coffee beans with different maturation states. By means of a statistical analysis, the relations between the features were obtained and, three main components were selected. These were subjected to a Bayesian classifier, which allowed to determine a full classification of the three types of grains, using the two main components and, two other combinations of the features, mainly color in the second Wavelet transformation filtering level.

 

REFERENCES

[1] G. I. Puerta Quintero, “Rendimientos y calidad de coffea arabica L., según el desarrollo del fruto y la remoción del mucílago,” Cenicafé, vol. 1, no. 61, pp. 67-89, 2010.
[2] G. Puerta Q., “Influencia de los granos de café cosechados verdes, en la calidad física y organoléptica de la bebida,” Cenicafé, vol. 2, no. 51, pp. 136-150, 2000.
[3] E. R. Arboleda, . A. C. Fajardo and R. P. Medina, “Classification of Coffee Bean Species Using Image Processing, Artificial Neural Network and KNearest Neighbors,” 2018 IEEE International Conference on Innovative Research and Development , pp. 1-5, 2018.
[4] R. H. M. H. J. H. C. P.-Z. C. E. G.-C. J. C. &. B.-C. C. A. Condori, “Automatic classification of physical defects in green coffee beans using CGLCM and SVM,” in XL Latin American Computing Conference (CLEI), 2014.
[5] K. Hameed, D. Chai and A. Rassau, “A comprehensive review of fruit and vegetable classification techniques,” Image and Vision Computing, vol. 80, pp. 24-44, 2018.
[6] T. Zhang, H.-M. Hu and B. Li, “A Naturalness Preserved Fast Dehazing Algorithm Using HSV Color Space,” IEEE access, vol. 6, pp. 10644-10649, 2018.
[7] W. W. S. Z. X. Z. J. &. F. J. Yang, “Greenness identification based on HSV decision tree,” Information Processing in Agriculture, vol. 2, no. 3-4, pp. 149-160, 2015.
[8] A. Nazir, R. Ashraf, T. Hamdani and N. Ali, “Content Based Image Retrieval System by using HSV Color Histogram, Discrete Wavelet Transform and Edge Histogram Descriptor,” in International Conference on Computing, Mathematics and Engineering Technologies, Taitung, 2018.
[9] C. . Y. Chong , . S. P. Lee and T. C. Ling, “Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach,” Information and Software Technology, pp. 1994-2012, 2013.
[10] W. Dong, R. Hu, Z. Chu and J. Zha, “Effect of different drying techniques on bioactive components, fatty acid composition, and volatile profile of robusta coffee beans,” Food Chemistry, pp. 121-130, 2017.
[11] K. Hron, M. Jelínková, P. Filzmoser and R. Kre, “Statistical analysis of wines using a robust compositional biplot,” Talanta, pp. 46-50, 2012.
[12] A. H. &. T. M. Jahromi, “A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features,” in Artificial Intelligence and Signal Processing Conference (AISP) , 2017.