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dc.contributor.authorde Castro, Ana I.
dc.contributor.authorPeña, José M.
dc.contributor.authorTorres-Sánchez, Jorge
dc.contributor.authorJiménez-Brenes, Francisco M.
dc.contributor.authorValencia-Gredilla, Francisco
dc.contributor.authorRecasens i Guinjuan, Jordi
dc.contributor.authorLópez-Granados, Francisca
dc.description.abstractThe establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management.ca_ES
dc.description.sponsorshipThis research was partly financed by the AGL2017-83325-C4-4R, AGL2017-83325-C4-2R, AGL2017-83325-C4-1R (Spanish Ministry of Science, Innovation and Universities and AEI/EU-FEDER funds) and the Intramural-CSIC projects (ref. 201840E002). Research of de Castro and F. Valencia-Gredilla were supported by the Juan de la Cierva-Incorporación Program and University of Lleida, respectivelyca_ES
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofRemote Sensing, 2020, vol. 12, núm. 1, article 56, p. 1-23ca_ES
dc.rightscc-by, (c) de Castro, Ana I. et al., 2019ca_ES
dc.subjectSite-specific weed managementca_ES
dc.subjectVegetation mappingca_ES
dc.subjectMachine learningca_ES
dc.titleMapping cynodon dactylon infesting cover crops with an automatic decision tree-OBIA procedure and UAV imagery for precision viticultureca_ES

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