Feature extraction for head and broken rice detection using image processing technique
Rice (Oryza Sativa) is the most important staple food for a large part of human population, especially in Southeast Asia such as Malaysia and Indonesia. Rice has been graded based on three main components namely: grain composition, milling quality and defective parts. Rice grading is important to en...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Faculty of Engineering, Universiti Putra Malaysia
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/33581/ http://psasir.upm.edu.my/id/eprint/33581/1/33581.pdf |
| _version_ | 1848847538134712320 |
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| author | Bibi Hanibah, Siti Sharifah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Wayayok, Aimrun |
| author_facet | Bibi Hanibah, Siti Sharifah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Wayayok, Aimrun |
| author_sort | Bibi Hanibah, Siti Sharifah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Rice (Oryza Sativa) is the most important staple food for a large part of human population, especially in Southeast Asia such as Malaysia and Indonesia. Rice has been graded based on three main components namely: grain composition, milling quality and defective parts. Rice grading is important to ensure only edible rice reaches the consumer standard. It also protects consumers from price manipulation. In this paper, a new approach of image processing technique has been developed to extract rice features. The features used were area, perimeter, minor axis length and major axis length of the rice. The rice images were first segmented automatically from its background by using Otsu’s method. Morphological operation was later being applied to the segmented image in order to eliminate unwanted region(s). Results from the experiment have shown that area gave more consistent results of head and broken rice detection compared to the other features. It is due to the difference in surface coverage area of the rice. Meanwhile, minor axis length gave the worst results due to same value for both broken and head rice. The method give the overall accuracy of 98% when tested using 600 samples of rice image taken from six different percentage of broken. |
| first_indexed | 2025-11-15T09:20:11Z |
| format | Conference or Workshop Item |
| id | upm-33581 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:20:11Z |
| publishDate | 2012 |
| publisher | Faculty of Engineering, Universiti Putra Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-335812017-02-03T09:41:54Z http://psasir.upm.edu.my/id/eprint/33581/ Feature extraction for head and broken rice detection using image processing technique Bibi Hanibah, Siti Sharifah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Wayayok, Aimrun Rice (Oryza Sativa) is the most important staple food for a large part of human population, especially in Southeast Asia such as Malaysia and Indonesia. Rice has been graded based on three main components namely: grain composition, milling quality and defective parts. Rice grading is important to ensure only edible rice reaches the consumer standard. It also protects consumers from price manipulation. In this paper, a new approach of image processing technique has been developed to extract rice features. The features used were area, perimeter, minor axis length and major axis length of the rice. The rice images were first segmented automatically from its background by using Otsu’s method. Morphological operation was later being applied to the segmented image in order to eliminate unwanted region(s). Results from the experiment have shown that area gave more consistent results of head and broken rice detection compared to the other features. It is due to the difference in surface coverage area of the rice. Meanwhile, minor axis length gave the worst results due to same value for both broken and head rice. The method give the overall accuracy of 98% when tested using 600 samples of rice image taken from six different percentage of broken. Faculty of Engineering, Universiti Putra Malaysia 2012 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/33581/1/33581.pdf Bibi Hanibah, Siti Sharifah and Bejo, Siti Khairunniza and Wan Ismail, Wan Ishak and Wayayok, Aimrun (2012) Feature extraction for head and broken rice detection using image processing technique. In: International Conference on Agricultural and Food Engineering for Life (Cafei2012), 26-28 Nov. 2012, Palm Garden Hotel, Putrajaya. (pp. 151-156). http://cafei.upm.edu.my/download.php?filename=/TechnicalPapers/CAFEi2012_47.pdf |
| spellingShingle | Bibi Hanibah, Siti Sharifah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Wayayok, Aimrun Feature extraction for head and broken rice detection using image processing technique |
| title | Feature extraction for head and broken rice detection using image processing technique |
| title_full | Feature extraction for head and broken rice detection using image processing technique |
| title_fullStr | Feature extraction for head and broken rice detection using image processing technique |
| title_full_unstemmed | Feature extraction for head and broken rice detection using image processing technique |
| title_short | Feature extraction for head and broken rice detection using image processing technique |
| title_sort | feature extraction for head and broken rice detection using image processing technique |
| url | http://psasir.upm.edu.my/id/eprint/33581/ http://psasir.upm.edu.my/id/eprint/33581/ http://psasir.upm.edu.my/id/eprint/33581/1/33581.pdf |