Determination of broken rice percentage using image processing technique

Physical rice grain composition is one of the components used for rice grading which involve the identification of head rice and broken rice. Rice grading is important to ensure only edible rice reaches the consumer standard. It also protects consumers from price manipulation. In this study, a new a...

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Main Author:	Hanibah, Siti Sharifah Bibi
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Image processing - Methodology Imaging systems. Rice - Quality control
Online Access:	http://psasir.upm.edu.my/id/eprint/56249/1/FK%202015%2066RR.pdf
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id	my-upm-ir.56249
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spelling	my-upm-ir.562492017-06-30T05:16:02Z Determination of broken rice percentage using image processing technique 2014-10 Hanibah, Siti Sharifah Bibi Physical rice grain composition is one of the components used for rice grading which involve the identification of head rice and broken rice. Rice grading is important to ensure only edible rice reaches the consumer standard. It also protects consumers from price manipulation. In this study, a new approach of image processing technique has been developed to detect and identify head and broken rice based on its physical properties i.e. area, perimeter, minor axis length and major axis length. The rice images were first segmented automatically from its background by using three different image segmentation methods, namely Mean Iterative,Median Iterative and Otsu's method. The Otsu's method provides satisfactory results. It only needs four iterations to complete the process of detection and identification, meanwhile Mean Iterative and Median Iterative takes 11 and 12 iterations, respectively. Furthermore, Otsu's method takes the average of four seconds to run the experiment, meanwhile Mean Iterative and Median Iterative takes average of 10 seconds to complete the experiment. Connected component analysis was later being applied to eliminate unwanted noise. Results from the statistical analysis have shown that area and perimeter give significant correlations with all of the other properties. However, area gives more consistent result with the value of correlation greater than 0.6 in all properties. Therefore, an area was later used as input parameter in developing a simple model of head and broken rice identification using a linear regression analysis. The model give promising results when tested with 0%,1%,5%,10%,15% and 20% of broken rice taken from 600 samples of rice images with the average percentage accuracy of 98%. Graphical User Interface (GUI) software was developed via MATLAB R2014a to help user directly access the percentage of broken rice from an image. Image processing - Methodology Imaging systems. Rice - Quality control 2014-10 Thesis http://psasir.upm.edu.my/id/eprint/56249/ http://psasir.upm.edu.my/id/eprint/56249/1/FK%202015%2066RR.pdf application/pdf en public masters Universiti Putra Malaysia Image processing - Methodology Imaging systems. Rice - Quality control
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Image processing - Methodology Imaging systems. Rice - Quality control
spellingShingle	Image processing - Methodology Imaging systems. Rice - Quality control Hanibah, Siti Sharifah Bibi Determination of broken rice percentage using image processing technique
description	Physical rice grain composition is one of the components used for rice grading which involve the identification of head rice and broken rice. Rice grading is important to ensure only edible rice reaches the consumer standard. It also protects consumers from price manipulation. In this study, a new approach of image processing technique has been developed to detect and identify head and broken rice based on its physical properties i.e. area, perimeter, minor axis length and major axis length. The rice images were first segmented automatically from its background by using three different image segmentation methods, namely Mean Iterative,Median Iterative and Otsu's method. The Otsu's method provides satisfactory results. It only needs four iterations to complete the process of detection and identification, meanwhile Mean Iterative and Median Iterative takes 11 and 12 iterations, respectively. Furthermore, Otsu's method takes the average of four seconds to run the experiment, meanwhile Mean Iterative and Median Iterative takes average of 10 seconds to complete the experiment. Connected component analysis was later being applied to eliminate unwanted noise. Results from the statistical analysis have shown that area and perimeter give significant correlations with all of the other properties. However, area gives more consistent result with the value of correlation greater than 0.6 in all properties. Therefore, an area was later used as input parameter in developing a simple model of head and broken rice identification using a linear regression analysis. The model give promising results when tested with 0%,1%,5%,10%,15% and 20% of broken rice taken from 600 samples of rice images with the average percentage accuracy of 98%. Graphical User Interface (GUI) software was developed via MATLAB R2014a to help user directly access the percentage of broken rice from an image.
format	Thesis
qualification_level	Master's degree
author	Hanibah, Siti Sharifah Bibi
author_facet	Hanibah, Siti Sharifah Bibi
author_sort	Hanibah, Siti Sharifah Bibi
title	Determination of broken rice percentage using image processing technique
title_short	Determination of broken rice percentage using image processing technique
title_full	Determination of broken rice percentage using image processing technique
title_fullStr	Determination of broken rice percentage using image processing technique
title_full_unstemmed	Determination of broken rice percentage using image processing technique
title_sort	determination of broken rice percentage using image processing technique
granting_institution	Universiti Putra Malaysia
publishDate	2014
url	http://psasir.upm.edu.my/id/eprint/56249/1/FK%202015%2066RR.pdf
_version_	1747812129921040384

Determination of broken rice percentage using image processing technique

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