Corn Seeds Identification Based on Shape and Colour Features
Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn se...
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| Format: | UMS Journal (OJS) |
| Language: | eng |
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Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
2020
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| Online Access: | https://journals.ums.ac.id/index.php/khif/article/view/10840 |
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| author | Yafie, Haddad Alwi Rachmawati, Ema Prakasa, Esa Nur, Amin |
| author_facet | Yafie, Haddad Alwi Rachmawati, Ema Prakasa, Esa Nur, Amin |
| author_sort | Yafie, Haddad Alwi |
| collection | OJS |
| description | Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals.ums.ac.id:article-10840 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2020 |
| publisher | Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia |
| record_format | ojs |
| spelling | oai:ojs2.journals.ums.ac.id:article-10840 Corn Seeds Identification Based on Shape and Colour Features Yafie, Haddad Alwi Rachmawati, Ema Prakasa, Esa Nur, Amin artificial neural network, eccentricity, feature extraction, region of interest Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%. Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia 2020-08-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals.ums.ac.id/index.php/khif/article/view/10840 10.23917/khif.v6i2.10840 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika; Vol. 6 No. 2 October 2020 Khazanah Informatika; Vol. 6 No. 2 October 2020 2477-698X 2621-038X eng https://journals.ums.ac.id/index.php/khif/article/view/10840/5942 Copyright (c) 2020 Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | artificial neural network, eccentricity, feature extraction, region of interest Yafie, Haddad Alwi Rachmawati, Ema Prakasa, Esa Nur, Amin Corn Seeds Identification Based on Shape and Colour Features |
| title | Corn Seeds Identification Based on Shape and Colour Features |
| title_full | Corn Seeds Identification Based on Shape and Colour Features |
| title_fullStr | Corn Seeds Identification Based on Shape and Colour Features |
| title_full_unstemmed | Corn Seeds Identification Based on Shape and Colour Features |
| title_short | Corn Seeds Identification Based on Shape and Colour Features |
| title_sort | corn seeds identification based on shape and colour features |
| topic | artificial neural network, eccentricity, feature extraction, region of interest |
| topic_facet | artificial neural network, eccentricity, feature extraction, region of interest |
| url | https://journals.ums.ac.id/index.php/khif/article/view/10840 |
| work_keys_str_mv | AT yafiehaddadalwi cornseedsidentificationbasedonshapeandcolourfeatures AT rachmawatiema cornseedsidentificationbasedonshapeandcolourfeatures AT prakasaesa cornseedsidentificationbasedonshapeandcolourfeatures AT nuramin cornseedsidentificationbasedonshapeandcolourfeatures |