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dc.contributor.authorGutiérrez, Salvador-
dc.contributor.authorTardáguila, Javier-
dc.contributor.authorFernández-Novales, Juan-
dc.contributor.authorDiago, María P.-
dc.date.accessioned2020-04-28T08:28:52Z-
dc.date.available2020-04-28T08:28:52Z-
dc.date.issued2019-01-
dc.identifierdoi: 10.1111/ajgw.12376-
dc.identifierissn: 1322-7130-
dc.identifiere-issn: 1755-0238-
dc.identifier.citationAustralian Journal of Grape and Wine Research 25(1): 127-133 (2019)-
dc.identifier.urihttp://hdl.handle.net/10261/209333-
dc.description.abstract[Background and Aims] Hyperspectral imaging (HSI) is used to assess fruit composition mostly indoor under controlled conditions. This work evaluates a HSI technique to measure TSS and anthocyanin concentration in wine grapes non‐destructively, in real time and in the vineyard. [Methods and Results] Hyperspectral images were acquired under natural illumination with a VIS–NIR hyperspectral camera (400–1000 nm) mounted on an all‐terrain vehicle moving at 5 km/h in a commercial Tempranillo vineyard in La Rioja, Spain. Measurements were taken on four dates during grape ripening in 2017. Grape composition was analysed on the grapes imaged, which was then used to develop spectral models, trained with support vector machines, to predict TSS and anthocyanin concentration. Regression models of TSS had determination coefficients (R2) of 0.91 for a fivefold cross validation [root mean squared error (RMSE) of 1.358°Brix] and 0.92 for the prediction of external samples (RMSE of 1.274°Brix). For anthocyanin concentration, R2 of 0.72 for cross validation (RMSE of 0.282 mg/g berry) and 0.83 for prediction (RMSE of 0.211 mg/g berry) was achieved. Spatial–temporal variation maps were developed for the four image acquisition dates during ripening. [Conclusions] These results suggest that potential for on‐the‐go HSI to automate the assessment of important grape compositional parameters in vineyard is promising. [Significance of the Study] The on‐the‐go HSI method described in this study could be automated and provide valuable information to improve winery and vineyard decisions and vineyard management.-
dc.description.sponsorshipThe project received funding from the European Union under grant agreement number 737669 (Vinescout project).Mr Salvador Gutiérrez would like to acknowledge theresearch funding FPI grant 299/2016 by Universidad de LaRioja, Gobierno de La Rioja. Dr Maria P. Diago is funded bythe Spanish Ministry of Science, Innovation and Universitywith a Ramon y Cajal grant RYC-2015-18429-
dc.languageeng-
dc.publisherAustralian Society of Viticulture and Oenology-
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/737669-
dc.rightsclosedAccess-
dc.subjectPlant phenotyping-
dc.subjectProximal sensing regression-
dc.subjectSensors-
dc.subjectSupport vector machine-
dc.titleOn-the-go hyperspectral imaging for the in-field estimation of grape composition-
dc.typeartículo-
dc.relation.publisherversionhttp://dx.doi.org/10.1111/ajgw.12376-
dc.date.updated2020-04-28T08:28:52Z-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.contributor.funderEuropean Commission-
dc.contributor.funderGobierno de La Rioja-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.contributor.orcidTardáguila, Javier [0000-0002-6639-8723]-
dc.contributor.orcidFernández-Novales, Juan [0000-0001-9973-2604]-
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