Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters
Background: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two‐dimensional (2D) and three‐dimensional (3D) machine vis...
| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley Online Library
2020
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/6787 https://onlinelibrary.wiley.com/doi/full/10.1002/jsfa.7675 |
| _version_ | 1855032489899196416 |
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| author | Tello-Moro, Javier Cubero, Sergio Blasco, José Tardáguila, Javier Aleixos, Nuria Ibanez, Javier |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Ibanez, Javier Tardáguila, Javier Tello-Moro, Javier |
| author_facet | Tello-Moro, Javier Cubero, Sergio Blasco, José Tardáguila, Javier Aleixos, Nuria Ibanez, Javier |
| author_sort | Tello-Moro, Javier |
| collection | ReDivia |
| description | Background:
Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two‐dimensional (2D) and three‐dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation.
Results:
The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R2 = 84.5 and 71.1%, respectively).
Conclusion:
The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry |
| format | article |
| id | ReDivia6787 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Wiley Online Library |
| publisherStr | Wiley Online Library |
| record_format | dspace |
| spelling | ReDivia67872025-04-25T14:47:49Z Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters Tello-Moro, Javier Cubero, Sergio Blasco, José Tardáguila, Javier Aleixos, Nuria Ibanez, Javier Grapevine clusters N01 Agricultural engineering Image analysis Background: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two‐dimensional (2D) and three‐dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. Results: The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R2 = 84.5 and 71.1%, respectively). Conclusion: The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry 2020-11-19T12:31:26Z 2020-11-19T12:31:26Z 2016 article acceptedVersion Tello, J., Cubero, S., Blasco, J., Tardaguila, J., Aleixos, N., & Ibáñez, J. (2016). Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters. Journal of the Science of Food and Agriculture, 96(13), 4575-4583. 1097-0010 http://hdl.handle.net/20.500.11939/6787 10.1002/jsfa.7675 https://onlinelibrary.wiley.com/doi/full/10.1002/jsfa.7675 en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess Wiley Online Library electronico |
| spellingShingle | Grapevine clusters N01 Agricultural engineering Image analysis Tello-Moro, Javier Cubero, Sergio Blasco, José Tardáguila, Javier Aleixos, Nuria Ibanez, Javier Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title | Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title_full | Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title_fullStr | Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title_full_unstemmed | Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title_short | Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters |
| title_sort | application of 2d and 3d image technologies to characterise morphological attributes of grapevine clusters |
| topic | Grapevine clusters N01 Agricultural engineering Image analysis |
| url | http://hdl.handle.net/20.500.11939/6787 https://onlinelibrary.wiley.com/doi/full/10.1002/jsfa.7675 |
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