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dc.contributor.authorMillán Prior, Borja 
dc.contributor.authorDiago, María Paz
dc.contributor.authorAquino Martín, Arturo 
dc.contributor.authorPalacios, Fernando
dc.contributor.authorTardaguila, Javier
dc.identifier.citationMillan, B., Diago, M. P., Aquino, A., Palacios, F., & Tardaguila, J. (2019). Vineyard pruning weight assessment by machine vision: towards an on-the-go measurement system. In OENO One (Vol. 53, Issue 2). Universite de Bordeaux.
dc.description.abstractAim: Pruning weight is an indicator of vegetative growth and vigour in grapevine. Traditionally, it is manually determined, which is time-consuming and labour-demanding. This study aims at providing a new, non-invasive and low-cost method for pruning weight estimation in commercial vineyards based on computer vision. Methods and results: The methodology relies on computer-based analysis of RGB images captured manually and on-the-go in a VSP Tempranillo vineyard. Firstly, the pruning weight estimation was evaluated using manually taken photographs using a controlled background. These images were analysed to generate a model of wood pruning weight estimation, resulting in a coefficient of determination (R2) of 0.91 (p<0.001) and a root-mean-square error (RMSE) of 87.7 g. After this, a mobile sensor platform (modified ATV) was used to take vine images automatically and on-the-go without background. These RGB images were analysed using a fully automated computer vision algorithm, resulting in R2 = 0.75 (p<0.001) and RMSE = 147.9 g. Finally, the mobile sensor platform was also used to sample a commercial VSP vineyard to map the spatial variability of wood pruning weight, and hereafter vine vigour. Conclusions: The results showed that the developed computer vision methodology was able to estimate the vine pruning weight in commercial vineyards and to map the spatial variation of the pruning weight across a vineyard. Significance and impact of the study: The presented methodology may become a valuable tool for the wine industry for rapid assessment and mapping of vine vigour. This information can be used to support decision making on pruning, fertilization and canopy management.es_ES
dc.description.sponsorshipWe would like to thank Ignacio Barrio and Saúl Río for their help collecting and analysing field data. Dr Maria P. Diago is funded by the Spanish Ministry of Science, Innovation and Universities with a Ramon y Cajal grant RYC-2015-18429. Borja Millán is recipient of a Juan de la CiervaFormación research contract (FJCI-2017-31824) funded by the Spanish Ministry of Science, Innovation and Universities. This work received funding from the European Community’s Seventh Framework Program (FP7/2007–2013) under Grant Agreement FP7-311775, Project Innovine.es_ES
dc.publisherInternational Viticulture and Enology Society (IVES)es_ES
dc.relation.isversionofPublisher’s version
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subject.otherImage analysises_ES
dc.subject.otherPrecision viticulturees_ES
dc.subject.otherNon-invasive sensing technologieses_ES
dc.subject.otherVitis vinifera L.es_ES
dc.titleVineyard pruning weight assessment by machine vision: towards an on-the-go measurement systemes_ES

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