Volume 3, Issue 2, June 2018, Page: 33-48
Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing
Thae Nu Wah, Department of Electronic Engineering, Technological University (Thanlyin), Yangon, Myanmar
Hla Myo Tun, Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar
Received: Aug. 12, 2018;       Accepted: Aug. 28, 2018;       Published: Sep. 25, 2018
DOI: 10.11648/j.mlr.20180302.14      View  338      Downloads  19
The paper presents the analysis on detection of chalkiness of Myanmar Rice using image processing with the help of MATLAB. Chalkiness is a major control in rice production because it is one of the key factors determining grain quality (appearance, processing, milling, storing, eating, and cooking quality) and price. Its reduction is a major goal, and the primary purpose of this study was to scrutinize the genetic basis of grain chalkiness. Recent researches have shown that elevated nighttime air temperatures (NTATs) could contribute to increased chalk and reduced milling quality. Machine vision has been used in a most application of grain classification to differentiate rice varieties based on special features such as shape, length, chalkiness, colour and internal damage of rice. There are many kinds of rice in Myanmar. Among them, the Enatha, KaungNyib, nurserySticky, Paw-San and Zee Yar are famous types of rice for daily usages in Myanmar. In this paper, the analysis has been emphasized on those kinds of rice with the help of image processing techniques. The detection method for rice chalkiness has been analysed on the various kinds of Myanmar rice such as Ematha (20%) 1.0A, KaungNyin3, nurserySticky110, Paw-San C and zee yar10. The results show that the rice chalkiness distribution function based on area of interest (location) and is could be measured with chalkiness intensity in this paper.
Detection Method, Chalkiness, Myanmar Rice, Digital Image Processing, Distribution Function
To cite this article
Thae Nu Wah, Hla Myo Tun, Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing, Machine Learning Research. Vol. 3, No. 2, 2018, pp. 33-48. doi: 10.11648/j.mlr.20180302.14
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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