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dc.contributor.authorViloria, Amelec
dc.contributor.authorRuiz Lázaro, Alex
dc.contributor.authorEcheverría González, Ana Maria
dc.contributor.authorPineda Lezama, Omar Bonerge
dc.contributor.authorLamby Barrios, Juan Guillermo
dc.contributor.authorLeon Castro, Nadia
dc.description.abstractAgriculture plays an important role in Latin American countries where the demand for provisions to reduce hunger and poverty represents a significant priority in order to improve the development and quality of life in the region. In this research, linear data analysis techniques and soil classification are reviewed through neural networks for decision making in agriculture. The results permit to conclude that precision agriculture, observation and control technologies are gaining ground, making it possible to determine the production demand in these countriesspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectNeural networksspa
dc.subjectAgricultural activityspa
dc.subjectPrecision agriculturespa
dc.subjectDecision makingspa
dc.subjectPrediction analysisspa
dc.titlePrediction of the efficiency for decision making in the agricultural sector through artificial intelligencespa
dc.typeArtículo de revistaspa
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Attribution-NonCommercial-NoDerivatives 4.0 International
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