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dc.creatorSilva, Jose
dc.creatorVarela Izquierdo, Noel
dc.creatorCabrera, Danelys
dc.creatorLezama, Omar
dc.date.accessioned2021-01-19T21:22:02Z
dc.date.available2021-01-19T21:22:02Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/11323/7720
dc.description.abstractGrass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them year-round, the ability of the ruminant to use fibrous supplies and be an economic source (Sánchez et al., Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, 2018, [1]). In this work, an application of neural networks was carried out in the forecasting of more accurate values of production and quality of grasslands.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.subjectArtificial intelligencespa
dc.subjectForagespa
dc.subjectGrassspa
dc.subjectNeural networksspa
dc.titlePrediction of the yield of grains through artificial intelligencespa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-7907-3_34spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-7907-3_34


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