Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing

Thae Nu Wah; Hla Myo Tun

doi:doi:10.11648/j.mlr.20180302.14

| Peer-Reviewed

Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing

Thae Nu Wah, Hla Myo Tun

Published in Machine Learning Research (Volume 3, Issue 2)

Received: 12 August 2018 Accepted: 28 August 2018 Published: 25 September 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

Published in	Machine Learning Research (Volume 3, Issue 2)
DOI	10.11648/j.mlr.20180302.14
Page(s)	33-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Detection Method, Chalkiness, Myanmar Rice, Digital Image Processing, Distribution Function

References

[1]	Fitzgerald MA, McCouch SR, Hall RD: Not just a grain of rice: the quest for quality. Trends Plant Sci 2009, 14:133–139.
[2]	Cheng SH, Zhuang JY, Fan YY, Du JH, Cao LY: Progress in research and development on hybrid rice: a super-domesticate in China. Ann Bot 2007, 100:959–966.
[3]	Zhang QF: Strategies for developing green super rice. Proc Natl Acad Sci U S A 2007, 104:16402–16409.
[4]	Wan XY, Weng JF, Zhai HQ, Wang JK, Lei CL, Liu XL, Guo T, Jiang L, Su N, Wan JM: Quantitative trait loci (QTL) analysis for rice grain width and fine mapping of an identified QTL allele gw-5 in a recombination hotspot region on Chromosome 5. Genetics 2008, 179:2239–2252.
[5]	Yoshioka Y, Iwata H, Tabata M, Ninomiya S, Ohsawa R: Chalkiness in rice: potential for evaluation with image analysis. Crop Sci 2007, 47:2113–2120.
[6]	Shen XP, Shen XY, Li G, Gon GP, Zhang HC: Effect of seeding time on chalkiness of liangyoupeijiu in Jiangsu rice growing areas at different latitudes. Chinese J Rice Sci 2007, 21:677–680.
[7]	Tin HQ, Berg T, Bjørnstad Å: Diversity and adaptation in rice varieties under static (ex situ) and dynamic (in situ) management. Euphytica 2001, 122:491–502.
[8]	Cheng FM, Zhong LJ, Wang F, Zhang GP: Differences in cooking and eating properties between chalky and translucent parts in rice grains. Food Chem 2005, 90:39–46.
[9]	Wang JK, Wan XY, Li HH, Pfeiffer WH, Crouch J, Wan JM: Application of identified QTL-marker associations in rice quality improvement through a design-breeding approach. Theor Appl Genet 2007, 115:87–100.
[10]	Liu X, Wan X, Ma X, Wan J: Dissecting the genetic basis for the effect of rice chalkiness, amylose content, protein content, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice using the chromosome segment substitution line population across eight environments. Genome 2011, 54:64–80.
[11]	Zheng L, Zhang W, Liu S, Chen L, Liu X, Chen X, Ma J, Chen W, Zhao Z, Jiang L, Wan J: Genetic relationship between grain chalkiness, protein content, and paste viscosity properties in a backcross inbred population of rice. J Cereal Sci 2012, 56:153–160.
[12]	Lisle, A. J., M. Martin, and M. A. Fitzgerald. 2000. Chalky and translucent rice grains differ in starch composition and structure and cooking properties. Cereal Chem. 77:627-632.
[13]	Kadan, R. S., R. J. Bryant, and J. A. Miller. 2008. Effects of milling on functional properties of rice flour. J. Food Sci. 73:E151-E154.
[14]	Cooper, N. T. W., T. J. Siebenmorgen, and P. A. Counce. 2008. Effects of nighttime temperature during kernel development on rice physicochemical properties. Cereal Chem. 85:276-282.
[15]	J. Hemming and T. Rath, “PA-Precision Agriculture,” Journal of Agricultural Engineering Research 78, 233-243 (2001).

Cite This Article

Plain Text BibTeX RIS

APA Style

Thae Nu Wah, Hla Myo Tun. (2018). Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Machine Learning Research, 3(2), 33-48. https://doi.org/10.11648/j.mlr.20180302.14

Copy | Download

ACS Style

Thae Nu Wah; Hla Myo Tun. Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Mach. Learn. Res. 2018, 3(2), 33-48. doi: 10.11648/j.mlr.20180302.14

Copy | Download

AMA Style

Thae Nu Wah, Hla Myo Tun. Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Mach Learn Res. 2018;3(2):33-48. doi: 10.11648/j.mlr.20180302.14

Copy | Download

@article{10.11648/j.mlr.20180302.14,
  author = {Thae Nu Wah and Hla Myo Tun},
  title = {Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing},
  journal = {Machine Learning Research},
  volume = {3},
  number = {2},
  pages = {33-48},
  doi = {10.11648/j.mlr.20180302.14},
  url = {https://doi.org/10.11648/j.mlr.20180302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20180302.14},
  abstract = {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.},
 year = {2018}
}

Copy | Download

TY - JOUR
T1 - Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing
AU - Thae Nu Wah
AU - Hla Myo Tun
Y1 - 2018/09/25
PY - 2018
N1 - https://doi.org/10.11648/j.mlr.20180302.14
DO - 10.11648/j.mlr.20180302.14
T2 - Machine Learning Research
JF - Machine Learning Research
JO - Machine Learning Research
SP - 33
EP - 48
PB - Science Publishing Group
SN - 2637-5680
UR - https://doi.org/10.11648/j.mlr.20180302.14
AB - 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.
VL - 3
IS - 2
ER -

Copy | Download

Author Information

Thae Nu Wah

Department of Electronic Engineering, Technological University (Thanlyin), Yangon, Myanmar
Hla Myo Tun

Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Thae Nu Wah, Hla Myo Tun. (2018). Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Machine Learning Research, 3(2), 33-48. https://doi.org/10.11648/j.mlr.20180302.14

Copy | Download

ACS Style

Thae Nu Wah; Hla Myo Tun. Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Mach. Learn. Res. 2018, 3(2), 33-48. doi: 10.11648/j.mlr.20180302.14

Copy | Download

AMA Style

Thae Nu Wah, Hla Myo Tun. Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing. Mach Learn Res. 2018;3(2):33-48. doi: 10.11648/j.mlr.20180302.14

Copy | Download

@article{10.11648/j.mlr.20180302.14,
  author = {Thae Nu Wah and Hla Myo Tun},
  title = {Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing},
  journal = {Machine Learning Research},
  volume = {3},
  number = {2},
  pages = {33-48},
  doi = {10.11648/j.mlr.20180302.14},
  url = {https://doi.org/10.11648/j.mlr.20180302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20180302.14},
  abstract = {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.},
 year = {2018}
}

Copy | Download