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dc.creatorSilva, Jesús
dc.creatorVarela Izquierdo, Noel
dc.creatorPineda, Omar
dc.date.accessioned2020-11-12T21:11:07Z
dc.date.available2020-11-12T21:11:07Z
dc.date.issued2020
dc.identifier.issn2194-5357
dc.identifier.urihttps://hdl.handle.net/11323/7293
dc.description.abstractCoffee is produced in Latin America, Africa and Asia, and is one of the most traded agricultural products in international markets. The coffee agribusiness has been diversified all over the world and constitutes an important source of employment, income and foreign exchange in many producing countries. In recent years, its global supply has been affected by adverse weather factors and pests such as rust, which has been reflected in a highly volatile international market for this product [1]. This paper shows a method for the detection of coffee crops and the presence of pests and diseases in the production of these crops, using multispectral images from the Landsat 8 satellite.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectCoffee productionspa
dc.subjectDetection of diseasesspa
dc.subjectMultispectral image analysisspa
dc.titleMultispectral image analysis for the detection of diseases in coffee productionspa
dc.typePreprintspa
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dc.source.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089716858&doi=10.1007%2f978-3-030-53036-5_21&partnerID=40&md5=a061f6acfd1ce0ab466fc6216508eea7spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-06-19
dc.type.hasversioninfo:eu-repo/semantics/draftspa


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