Show simple item record

dc.creatorViloria, Amelec
dc.creatorRuiz Lázaro, Alex
dc.creatorEcheverría González, Ana Maria
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorLamby Barrios, Juan Guillermo
dc.creatorLeon Castro, Nadia
dc.date.accessioned2021-01-18T20:49:15Z
dc.date.available2021-01-18T20:49:15Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/11323/7711
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.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.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.typearticlespa
dcterms.references1. Abraira V (2014) El Índice Kappa. Unidad de Bioestadística Clínica. 2014. 89, Montreal: sf, 2014, SEMERGEN, vol 12, pp 128–130spa
dcterms.references2. Apraéz BE (2015) La responsabilidad por producto defectuoso en la Ley 1480 de 2011. Explicación a partir de una obligación de seguridad de origen legal y constitucional. Revista de Derecho Privado (28):367–399spa
dcterms.references3. FAO (2017) Organización de las Naciones Unidas para la Agricultura y Alimentación. Datos estadísticos. Recuperado el 09 de enero de 2018spa
dcterms.references4. Garcia MI (2003) Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales. Universitas Scientiarum, vol 8, pp 45–50spa
dcterms.references5. Matich DJ (2001) “Redes Neuronales: Conceptos básicos y aplicaciones”, Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación Ispa
dcterms.references6. Mercado D, Pedraza L, Martínez E (2015) Comparación de Redes Neuronales aplicadas a la predicción de Series de Tiempo. Prospectiva 13(2):88–95spa
dcterms.references7. Wu Q, Yan HS, Yang HB (2008) A forecasting model based support vector machine and particle swarm optimization. In: 2008 Workshop on power electronics and intelligent transportation system, pp 218–222spa
dcterms.references8. Clements CF, Ozgul A (2016) Rate of forcing and the forecastability of critical transitions. Ecol Evol 6:7787–7793spa
dcterms.references9. Comisión Económica para América Latina y el Caribe -CEPAL- (2013) Visión agrícola del TLC entre Colombia y Estados Unidos: preparación, negociación, implementación y aprovechamiento. Serie Estudios y Perspectivas, 25, 87spa
dcterms.references10. Henao-Rodríguez C, Lis-Gutiérrez JP, Gaitán-Angulo M, Malagón LE, Viloria A (2018) Econometric analysis of the industrial growth determinants in Colombia. In: Australasian database conference, Springer, Cham, pp 316–321spa
dcterms.references11. Viloria A, Gaitan-Angulo M (2016) Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J Sci Technol 9(47).spa
dcterms.references12. Song YY, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch 27:130spa
dcterms.references13. Mehdiyev N, Lahann J, Emrich A, Enke D, Fettke P, Loos P (2017) Time series classification using deep learning for process planning: a case from the process industry. Proc Comput Sci 114:242–249spa
dcterms.references14. Wang S, Liu P, Zhang Z, Zhang Y, Song C et al (2016) Development of management methods for “bohai sea granary” data. J Chinese Agric Mechanization 37(3):270–275spa
dcterms.references15. Liu B, Shao D, Shen X (2013) Reference crop evaportranspiration forecasting model for BP neural networks based on wavelet transform. Eng J Wuhan 34:69-73 [7-5g, Guangzhou: IEEE, 2013, 5102-2575]spa
dcterms.references16. Silveira CT (2013) Soil prediction using artificial neural networks and topographic attributes. Geoderma. 2013, IEEE, pp 192–197spa
dcterms.references17. Valiente Ó (2013) Education: current practice, international comparative research evidence and policy implications. OCDE, Chicago, pp 44–52 [133-133234-33]spa
dcterms.references18. Andrecut MK, Ali MA (2012) Quantum neural network model. 2012. Int J Mod Phys 12:75–88 [1573-1332]spa
dcterms.references19. Srinivan A (2013) Handbook of precision agriculture: principles and applications. CRC, New York, 683pspa
dcterms.references20. Rodrigues MS, Corá JE, Fernandes C (2014) Spatial relationships between soil attributes and corn yield in no-tillage system. J Soil Sci Plant Nutr 1:367–379 [1806-9657]spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_91spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-7234-0_91


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International