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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.26754/jjii3a.4868</dc:identifier><dc:language>eng</dc:language><dc:creator>Lacueva Pérez, Francisco José</dc:creator><dc:creator>Ilarri Artigas, Sergio</dc:creator><dc:creator>Del Hoyo, Rafael</dc:creator><dc:creator>Barriuso, Juan José</dc:creator><dc:title>Improving Grapevine Sustainability through Multifactorial Machine Learning Application</dc:title><dc:identifier>ART-2021-143814</dc:identifier><dc:description>Wine farms have to adapt their activities to achieve sustainable development goals. Our goal is to contribute to this adaptation by developing Machine Learning models to predict phenology and pest risk with the aim of reducing applied phytosanitary treatments.</dc:description><dc:date>2021</dc:date><dc:source>http://zaguan.unizar.es/record/153623</dc:source><dc:doi>10.26754/jjii3a.4868</dc:doi><dc:identifier>http://zaguan.unizar.es/record/153623</dc:identifier><dc:identifier>oai:zaguan.unizar.es:153623</dc:identifier><dc:identifier.citation>Jornada de jóvenes investigadores del I3A 8 (2021), [2 pp.]</dc:identifier.citation><dc:rights>by-nc-nd</dc:rights><dc:rights>https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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