A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition
Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray sca...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/5137317 |
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| _version_ | 1832559237551620096 |
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| author | José García Christopher Pope Francisco Altimiras |
| author_facet | José García Christopher Pope Francisco Altimiras |
| author_sort | José García |
| collection | DOAJ |
| description | Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud. |
| format | Article |
| id | doaj-art-b846c4fa38724ed8874d22595cd8b9c0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b846c4fa38724ed8874d22595cd8b9c02025-02-03T01:30:34ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/51373175137317A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana RecognitionJosé García0Christopher Pope1Francisco Altimiras2Telefonica Investigación y Desarrollo, Santiago, ChileTelefonica Investigación y Desarrollo, Santiago, ChileTelefonica Investigación y Desarrollo, Santiago, ChileEarly detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.http://dx.doi.org/10.1155/2017/5137317 |
| spellingShingle | José García Christopher Pope Francisco Altimiras A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition Complexity |
| title | A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition |
| title_full | A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition |
| title_fullStr | A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition |
| title_full_unstemmed | A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition |
| title_short | A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition |
| title_sort | distributed k means segmentation algorithm applied to lobesia botrana recognition |
| url | http://dx.doi.org/10.1155/2017/5137317 |
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