Durian species recognition system based on global shape representations and k-nearest neighbors
Many fruit recognition systems today are designed to classify different type of fruits but there is no content-based fruit recognition system focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68928/ http://psasir.upm.edu.my/id/eprint/68928/1/Durian%20species%20recognition%20system%20based%20on%20global%20shape%20representations%20and%20k-nearest%20neighbors.pdf |
| _version_ | 1848856265596338176 |
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| author | Nyon, Xin Yi Mustaffa, Mas Rina Abdullah, Lili Nurliyana Nasharuddin, Nurul Amelina |
| author_facet | Nyon, Xin Yi Mustaffa, Mas Rina Abdullah, Lili Nurliyana Nasharuddin, Nurul Amelina |
| author_sort | Nyon, Xin Yi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Many fruit recognition systems today are designed to classify different type of fruits but there is no content-based fruit recognition system focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same color from green to yellowish brown with slightly different shape of thorns and it is hard to differentiate them with the current methods. Sometimes it is even hard for general consumers to differentiate durian species by themselves. This work aims to contribute to an automatic content-based durian species recognition that will be able to assist users in differentiating various species of durian. Few global contour-based and region-based shape descriptors such as area, perimeter, and circularity are computed as feature vectors and K-Nearest Neighbors algorithm is used to classify the durian based on the extracted features. 10-fold cross-validation is used to evaluate the proposed system. Experimental results have shown that the proposed feature extraction method for the durian species recognition system has successfully obtained a positive recognition rate of 100%. |
| first_indexed | 2025-11-15T11:38:55Z |
| format | Conference or Workshop Item |
| id | upm-68928 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:38:55Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-689282020-05-25T01:49:48Z http://psasir.upm.edu.my/id/eprint/68928/ Durian species recognition system based on global shape representations and k-nearest neighbors Nyon, Xin Yi Mustaffa, Mas Rina Abdullah, Lili Nurliyana Nasharuddin, Nurul Amelina Many fruit recognition systems today are designed to classify different type of fruits but there is no content-based fruit recognition system focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same color from green to yellowish brown with slightly different shape of thorns and it is hard to differentiate them with the current methods. Sometimes it is even hard for general consumers to differentiate durian species by themselves. This work aims to contribute to an automatic content-based durian species recognition that will be able to assist users in differentiating various species of durian. Few global contour-based and region-based shape descriptors such as area, perimeter, and circularity are computed as feature vectors and K-Nearest Neighbors algorithm is used to classify the durian based on the extracted features. 10-fold cross-validation is used to evaluate the proposed system. Experimental results have shown that the proposed feature extraction method for the durian species recognition system has successfully obtained a positive recognition rate of 100%. IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68928/1/Durian%20species%20recognition%20system%20based%20on%20global%20shape%20representations%20and%20k-nearest%20neighbors.pdf Nyon, Xin Yi and Mustaffa, Mas Rina and Abdullah, Lili Nurliyana and Nasharuddin, Nurul Amelina (2018) Durian species recognition system based on global shape representations and k-nearest neighbors. In: 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP'18), 26-28 Mar. 2018, Le Méridien Kota Kinabalu, Sabah, Malaysia. (pp. 79-84). 10.1109/INFRKM.2018.8464795 |
| spellingShingle | Nyon, Xin Yi Mustaffa, Mas Rina Abdullah, Lili Nurliyana Nasharuddin, Nurul Amelina Durian species recognition system based on global shape representations and k-nearest neighbors |
| title | Durian species recognition system based on global shape representations and k-nearest neighbors |
| title_full | Durian species recognition system based on global shape representations and k-nearest neighbors |
| title_fullStr | Durian species recognition system based on global shape representations and k-nearest neighbors |
| title_full_unstemmed | Durian species recognition system based on global shape representations and k-nearest neighbors |
| title_short | Durian species recognition system based on global shape representations and k-nearest neighbors |
| title_sort | durian species recognition system based on global shape representations and k-nearest neighbors |
| url | http://psasir.upm.edu.my/id/eprint/68928/ http://psasir.upm.edu.my/id/eprint/68928/ http://psasir.upm.edu.my/id/eprint/68928/1/Durian%20species%20recognition%20system%20based%20on%20global%20shape%20representations%20and%20k-nearest%20neighbors.pdf |