A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering
The anti-counterfeiting of agricultural products plays an important role in protecting the rights and interests of consumers and maintaining the healthy development of the food market. Traditional anti-counterfeiting technology mainly relies on anti-counterfeiting features of packaging or labeling,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Maximum Academic Press
2023-01-01
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| Series: | Food Innovation and Advances |
| Subjects: | |
| Online Access: | https://www.maxapress.com/article/doi/10.48130/FIA-2023-0004 |
| _version_ | 1827339922542952448 |
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| author | Chaoyu Shen Yiqin Zhang Luyao Chen Adele Lu Jia Jiankang Cao Weibo Jiang |
| author_facet | Chaoyu Shen Yiqin Zhang Luyao Chen Adele Lu Jia Jiankang Cao Weibo Jiang |
| author_sort | Chaoyu Shen |
| collection | DOAJ |
| description | The anti-counterfeiting of agricultural products plays an important role in protecting the rights and interests of consumers and maintaining the healthy development of the food market. Traditional anti-counterfeiting technology mainly relies on anti-counterfeiting features of packaging or labeling, which has the risk of being copied and reused. Biological fingerprint anti-counterfeiting is a method of anti-counterfeiting that takes the biological fingerprint of agricultural products as the anti-counterfeiting feature. This paper aims to take the distribution of lenticels on the surface of mango as a biological fingerprint, and propose a mango biological fingerprint anti-counterfeiting method. As the mango ripens, the peel color of mango will change significantly, which will affect the accuracy of anti-counterfeiting identification. In this paper, the images of ripe mangoes are classified by Fuzzy C-means clustering, and appropriate image enhancement technology is used to highlight the features. The results show that the mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering has good accuracy and robustness, and effectively reduces the impact of peel color change on anti-counterfeiting identification during mango ripening. These results support that it is feasible to use the lenticels distribution of mango as a biological fingerprint. In this paper, a computer vision anti-counterfeiting method based on lenticels distribution is proposed. |
| first_indexed | 2024-03-07T20:09:36Z |
| format | Article |
| id | doaj.art-1abbb64dabaf4e0089bb2db8cb960cea |
| institution | Directory Open Access Journal |
| issn | 2836-774X |
| language | English |
| last_indexed | 2024-03-07T20:09:36Z |
| publishDate | 2023-01-01 |
| publisher | Maximum Academic Press |
| record_format | Article |
| series | Food Innovation and Advances |
| spelling | doaj.art-1abbb64dabaf4e0089bb2db8cb960cea2024-02-28T01:41:27ZengMaximum Academic PressFood Innovation and Advances2836-774X2023-01-0121212710.48130/FIA-2023-0004FIA-2023-0004A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clusteringChaoyu Shen0Yiqin Zhang1Luyao Chen2Adele Lu Jia3Jiankang Cao4Weibo Jiang5College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR ChinaThe anti-counterfeiting of agricultural products plays an important role in protecting the rights and interests of consumers and maintaining the healthy development of the food market. Traditional anti-counterfeiting technology mainly relies on anti-counterfeiting features of packaging or labeling, which has the risk of being copied and reused. Biological fingerprint anti-counterfeiting is a method of anti-counterfeiting that takes the biological fingerprint of agricultural products as the anti-counterfeiting feature. This paper aims to take the distribution of lenticels on the surface of mango as a biological fingerprint, and propose a mango biological fingerprint anti-counterfeiting method. As the mango ripens, the peel color of mango will change significantly, which will affect the accuracy of anti-counterfeiting identification. In this paper, the images of ripe mangoes are classified by Fuzzy C-means clustering, and appropriate image enhancement technology is used to highlight the features. The results show that the mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering has good accuracy and robustness, and effectively reduces the impact of peel color change on anti-counterfeiting identification during mango ripening. These results support that it is feasible to use the lenticels distribution of mango as a biological fingerprint. In this paper, a computer vision anti-counterfeiting method based on lenticels distribution is proposed.https://www.maxapress.com/article/doi/10.48130/FIA-2023-0004biological fingerprintanti-counterfeiting of agricultural productsfuzzy c-means clusteringcomputer visionmango |
| spellingShingle | Chaoyu Shen Yiqin Zhang Luyao Chen Adele Lu Jia Jiankang Cao Weibo Jiang A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering Food Innovation and Advances biological fingerprint anti-counterfeiting of agricultural products fuzzy c-means clustering computer vision mango |
| title | A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering |
| title_full | A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering |
| title_fullStr | A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering |
| title_full_unstemmed | A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering |
| title_short | A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering |
| title_sort | mango biological fingerprint anti counterfeiting method based on fuzzy c means clustering |
| topic | biological fingerprint anti-counterfeiting of agricultural products fuzzy c-means clustering computer vision mango |
| url | https://www.maxapress.com/article/doi/10.48130/FIA-2023-0004 |
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