Development of an application (INDITES software) that allows to integrate spatial and temporal information of a vineyard for the development of the digital terroir.

CIGR Proceedings

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Title Development of an application (INDITES software) that allows to integrate spatial and temporal information of a vineyard for the development of the digital terroir.
 
Creator Stanley Best; Chilean Agricultural Research Institute (INIA)
Lorenzo León; Chilean Agricultural Research Institute (INIA)
Rodrigo Quintana; Chilean Agricultural Research Institute (INIA)
 
Description The terroir has been recognized as an important factor in wine quality and style, especially in European vineyards. There is currently a need for quantification of the factors that influence the definition of terroir, incorporating indexes that quantify variables such as soil, plant and climate, which has led to the definition of “Digital Terroir ". This paper proposes a methodology to develop the “digital terroir" through use of emerging technologies, as current procedures that should be use for the study and define of terroir which suffer from having replicable protocols. The study took place in Valdivieso Vineyard, Curicó, Chile, during the 2012 and 2013 seasons, under the Var. Cabernet Sauvignon, Merlot and Carmenere. The Ferari index (MULTIPLEX RESEARCH ™, FORCE- A), was used for the grapes quality quantification, which was obtained from field samples by a high density grid (20x20 m). Moreover, the soil and plant information was obtained by the use of equipment as follows, electrical conductivity (EM38), topography and exposure (RTK) and NDVI (Tetracam ADC). From the fruit quality index distribution curve (Ferari), 7 rated strata was developed by variety and year, which was used for training the respective model classification of the variables associated with the site. The classification algorithms were based on qualifying Boosting and vector machines (SVM). For model training, 75 % of the data was used and allowed the remaining 25 % to verify the calculation error (control data). The classification results were 95 %, 90 % and 80 %, of well classified area (R2>0.9 and Mean Absolute Error < 0.1) for Var.Carmenere, Cabernet Sauvignon and Merlot, respectively. Finally, the well defined grape quality area develop could be used for differential harvest and could be use for vineyard management when increase the yield it is the main goal. The described procedure and results are a keystone for the application of INDITES software presented on this work.
 
Publisher CIGR Proceedings
 
Contributor
 
Date 2015-02-09
 
Type Non-refereed
 
Format application/pdf
 
Identifier http://journals.sfu.ca/cigrp/index.php/Proc/article/view/163
 
Source CIGR Proceedings; 2014 World Congress on Computers in Agriculture
 
Language en
 

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