RT Journal Article
T1 Vineyard pruning weight assessment by machine vision: towards an on-the-go measurement system
A1 Millán Prior, Borja
A1 Diago, María Paz
A1 Aquino Martín, Arturo
A1 Palacios, Fernando
A1 Tardaguila, Javier
AB Aim: 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.
PB International Viticulture and Enology Society (IVES)
SN 2494-1271
YR 2019
FD 2019
LK http://hdl.handle.net/10272/20911
UL http://hdl.handle.net/10272/20911
LA eng
NO Millan, 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. https://doi.org/10.20870/oeno-one.2019.53.2.2416
NO We would like to thankIgnacio Barrio and Saúl Río for their helpcollecting and analysing field data. Dr Maria P.Diago is funded by the Spanish Ministry ofScience, Innovation and Universities with aRamon y Cajal grant RYC-2015-18429. BorjaMillán 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 receivedfunding from the European Community’sSeventh Framework Program (FP7/2007–2013)under Grant Agreement FP7-311775, ProjectInnovine.
DS Repositorio Institucional de la Universidad de Huelva
RD 30 may 2026