Diago, MP.; Tardaguila, J.; Aleixos Borrás, MN.; Millan, B.; Prats-Montalbán, JM.; Cubero, S.; Blasco Ivars, J. (2015). Assessment of cluster yield components by image analysis. Journal of the Science of Food and Agriculture. 95(6):1274-1282. https://doi.org/10.1002/jsfa.6819
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/152804
Title:
|
Assessment of cluster yield components by image analysis
|
Author:
|
Diago, Maria P.
Tardaguila, Javier
Aleixos Borrás, María Nuria
Millan, Borja
Prats-Montalbán, José Manuel
Cubero, Sergio
BLASCO IVARS, JOSE
|
UPV Unit:
|
Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària
Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
|
Issued date:
|
|
Abstract:
|
[EN] 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. ...[+]
[EN] 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 bymeans 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
[-]
|
Subjects:
|
Vitis vinifera L
,
Cluster weight
,
Berry number per cluster
,
Berry weight
,
LIP Canny
,
Hough Transform
|
Copyrigths:
|
Cerrado |
Source:
|
Journal of the Science of Food and Agriculture. (issn:
0022-5142
)
|
DOI:
|
10.1002/jsfa.6819
|
Publisher:
|
John Wiley & Sons
|
Publisher version:
|
https://doi.org/10.1002/jsfa.6819
|
Project ID:
|
MINECO/AGL2011-23673
UPV/UPV-SP10120276
INIA/RTA2012-00062-C04-01
INSTITUTO NACIONAL DE INV. Y TECNOL. AGRARIA Y ALIMENTARIA /RTA2012-00062-C04-03
|
Thanks:
|
This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors wish also to thank the MINECO which provided support ...[+]
This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors wish also to thank the MINECO which provided support for this research work through project AGL2011-23673 and also the UPV project UPV-SP10120276
[-]
|
Type:
|
Artículo
|