Assessment of cluster yield components by image analysis
Background: Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to deter...
| Main Authors: | , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11939/5136 |
| _version_ | 1855491873427161088 |
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| author | Diago-Santamaría, María P. Tardáguila, Javier Aleixos, Nuria Millan, Borja Prats-Montalbán, José M. Cubero, Sergio Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Diago-Santamaría, María P. Millan, Borja Prats-Montalbán, José M. Tardáguila, Javier |
| author_facet | Diago-Santamaría, María P. Tardáguila, Javier Aleixos, Nuria Millan, Borja Prats-Montalbán, José M. Cubero, Sergio Blasco, José |
| author_sort | Diago-Santamaría, María P. |
| collection | ReDivia |
| description | Background:
Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way.
Results:
Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R-2 between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%.
Conclusion:
The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. (c) 2014 Society of Chemical Industry |
| format | Artículo |
| id | ReDivia5136 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia51362025-04-25T14:45:29Z Assessment of cluster yield components by image analysis Diago-Santamaría, María P. Tardáguila, Javier Aleixos, Nuria Millan, Borja Prats-Montalbán, José M. Cubero, Sergio Blasco, José Background: Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. Results: Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R-2 between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. Conclusion: The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. (c) 2014 Society of Chemical Industry 2017-06-01T10:11:46Z 2017-06-01T10:11:46Z 2015 APR 2015 article Diago, M. P., Tardaguila, J., Aleixos, N., Millan, Borja, Prats-Montalban, J.M., Cubero, Sergio, Blasco, J. (2015). Assessment of cluster yield components by image analysis. Journal of the science of food and agriculture, 95(6), 1274-1282. 0022-5142 http://hdl.handle.net/20.500.11939/5136 10.1002/jsfa.6819 en openAccess Impreso |
| spellingShingle | Diago-Santamaría, María P. Tardáguila, Javier Aleixos, Nuria Millan, Borja Prats-Montalbán, José M. Cubero, Sergio Blasco, José Assessment of cluster yield components by image analysis |
| title | Assessment of cluster yield components by image analysis |
| title_full | Assessment of cluster yield components by image analysis |
| title_fullStr | Assessment of cluster yield components by image analysis |
| title_full_unstemmed | Assessment of cluster yield components by image analysis |
| title_short | Assessment of cluster yield components by image analysis |
| title_sort | assessment of cluster yield components by image analysis |
| url | http://hdl.handle.net/20.500.11939/5136 |
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