Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/193220
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Image analysis-based modelling for flower number estimation in grapevine |
Autor: | Millán Prior, Borja CSIC ORCID; Aquino, Arturo CSIC ORCID; Diago, Maria P. CSIC ORCID; Tardáguila, Javier CSIC ORCID | Palabras clave: | Fruit set rate Yield prediction Computer vision Flowering Multi‐variety linear models Non‐linear models |
Fecha de publicación: | feb-2017 | Editor: | John Wiley & Sons | Citación: | Journal of the Science of Food and Agriculture 97(3): 784-792 (2017) | Resumen: | [Background] Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety‐independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated. [Results] Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single‐variable and multivariable) and a nonlinear variety‐independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R2 = 0.79) and average berry weight (R2 = 0.91) were also tested. [Conclusion] This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non‐invasive and reliable tool for estimation of yield at harvest | Versión del editor: | http://dx.doi.org/10.1002/jsfa.7797 | URI: | http://hdl.handle.net/10261/193220 | DOI: | 10.1002/jsfa.7797 | ISSN: | 0022-5142 | E-ISSN: | 1097-0010 |
Aparece en las colecciones: | (ICVV) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
accesoRestringido.pdf | 15,35 kB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
29
checked on 26-abr-2024
WEB OF SCIENCETM
Citations
24
checked on 26-feb-2024
Page view(s)
179
checked on 28-abr-2024
Download(s)
21
checked on 28-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.