English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/144428
COMPARTIR / IMPACTO:
Estadísticas
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Título

Assessment of cluster yield components by image analysis

AutorDiago, Maria P. ; Tardáguila, Javier ; Aleixos, Nuria; Millán Prior, Borja ; Prats-Montalbán, J. M.; Cubero, Sergio; Blasco, José
Palabras claveBerry number per cluster
LIP–Canny
Hough Transform
Berry weight
Cluster weight
Vitis vinifera L
Fecha de publicaciónabr-2015
EditorJohn Wiley & Sons
CitaciónJournal of the Science of Food and Agriculture 95(6): 1274-1282 (2015)
Resumen[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 (R2 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. © 2014 Society of Chemical Industry
Versión del editorhttp://doi.org/10.1002/jsfa.6819
URIhttp://hdl.handle.net/10261/144428
DOI10.1002/jsfa.6819
Identificadoresissn: 1097-0010
e-issn: 0022-5142
Aparece en las colecciones: (ICVV) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 

Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.