Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence
Agriculture 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 classificat...
- Autores:
-
Viloria, Amelec
Ruiz Lázaro, Alex
Echeverría González, Ana Maria
Pineda Lezama, Omar Bonerge
Lamby Barrios, Juan Guillermo
Leon Castro, Nadia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7711
- Acceso en línea:
- https://hdl.handle.net/11323/7711
https://doi.org/10.1007/978-981-15-7234-0_91
https://repositorio.cuc.edu.co/
- Palabra clave:
- Neural networks
Agricultural activity
Precision agriculture
Decision making
Prediction analysis
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| title |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| spellingShingle |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence Neural networks Agricultural activity Precision agriculture Decision making Prediction analysis |
| title_short |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| title_full |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| title_fullStr |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
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Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| title_sort |
Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence |
| dc.creator.fl_str_mv |
Viloria, Amelec Ruiz Lázaro, Alex Echeverría González, Ana Maria Pineda Lezama, Omar Bonerge Lamby Barrios, Juan Guillermo Leon Castro, Nadia |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Ruiz Lázaro, Alex Echeverría González, Ana Maria Pineda Lezama, Omar Bonerge Lamby Barrios, Juan Guillermo Leon Castro, Nadia |
| dc.subject.spa.fl_str_mv |
Neural networks Agricultural activity Precision agriculture Decision making Prediction analysis |
| topic |
Neural networks Agricultural activity Precision agriculture Decision making Prediction analysis |
| description |
Agriculture 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 countries |
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2021 |
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2021-01-18T20:49:15Z |
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2021 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
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1. Abraira V (2014) El Índice Kappa. Unidad de Bioestadística Clínica. 2014. 89, Montreal: sf, 2014, SEMERGEN, vol 12, pp 128–130 2. 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):367–399 3. FAO (2017) Organización de las Naciones Unidas para la Agricultura y Alimentación. Datos estadísticos. Recuperado el 09 de enero de 2018 4. 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 45–50 5. Matich DJ (2001) “Redes Neuronales: Conceptos básicos y aplicaciones”, Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I 6. Mercado D, Pedraza L, Martínez E (2015) Comparación de Redes Neuronales aplicadas a la predicción de Series de Tiempo. Prospectiva 13(2):88–95 7. 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 218–222 8. Clements CF, Ozgul A (2016) Rate of forcing and the forecastability of critical transitions. Ecol Evol 6:7787–7793 9. 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, 87 10. 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 316–321 11. 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). 12. Song YY, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch 27:130 13. 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 114:242–249 14. 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):270–275 15. 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] 16. Silveira CT (2013) Soil prediction using artificial neural networks and topographic attributes. Geoderma. 2013, IEEE, pp 192–197 17. Valiente Ó (2013) Education: current practice, international comparative research evidence and policy implications. OCDE, Chicago, pp 44–52 [133-133234-33] 18. Andrecut MK, Ali MA (2012) Quantum neural network model. 2012. Int J Mod Phys 12:75–88 [1573-1332] 19. Srinivan A (2013) Handbook of precision agriculture: principles and applications. CRC, New York, 683p 20. 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] |
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Viloria, AmelecRuiz Lázaro, AlexEcheverría González, Ana MariaPineda Lezama, Omar BonergeLamby Barrios, Juan GuillermoLeon Castro, Nadia2021-01-18T20:49:15Z2021-01-18T20:49:15Z2021https://hdl.handle.net/11323/7711https://doi.org/10.1007/978-981-15-7234-0_91Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Agriculture 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 countriesViloria, AmelecRuiz Lázaro, Alex-will be generated-orcid-0000-0002-5974-2864-600Echeverría González, Ana MariaPineda Lezama, Omar BonergeLamby Barrios, Juan Guillermo-will be generated-orcid-0000-0001-5358-0270-600Leon Castro, Nadiaapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_91Neural networksAgricultural activityPrecision agricultureDecision makingPrediction analysisPrediction of the efficiency for decision making in the agricultural sector through artificial intelligenceArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Abraira V (2014) El Índice Kappa. Unidad de Bioestadística Clínica. 2014. 89, Montreal: sf, 2014, SEMERGEN, vol 12, pp 128–1302. 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):367–3993. FAO (2017) Organización de las Naciones Unidas para la Agricultura y Alimentación. Datos estadísticos. Recuperado el 09 de enero de 20184. 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 45–505. Matich DJ (2001) “Redes Neuronales: Conceptos básicos y aplicaciones”, Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I6. Mercado D, Pedraza L, Martínez E (2015) Comparación de Redes Neuronales aplicadas a la predicción de Series de Tiempo. Prospectiva 13(2):88–957. 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 218–2228. Clements CF, Ozgul A (2016) Rate of forcing and the forecastability of critical transitions. Ecol Evol 6:7787–77939. 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, 8710. 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 316–32111. 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).12. Song YY, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch 27:13013. 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 114:242–24914. 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):270–27515. 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]16. Silveira CT (2013) Soil prediction using artificial neural networks and topographic attributes. Geoderma. 2013, IEEE, pp 192–19717. Valiente Ó (2013) Education: current practice, international comparative research evidence and policy implications. OCDE, Chicago, pp 44–52 [133-133234-33]18. Andrecut MK, Ali MA (2012) Quantum neural network model. 2012. Int J Mod Phys 12:75–88 [1573-1332]19. Srinivan A (2013) Handbook of precision agriculture: principles and applications. CRC, New York, 683p20. 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]PublicationORIGINALPrediction of the efficiency for decision making in the agricultural sector through artificial intelligence.pdfPrediction of the efficiency for decision making in the agricultural sector through artificial intelligence.pdfapplication/pdf95269https://repositorio.cuc.edu.co/bitstreams/545791b6-c37b-4c8b-a5aa-5e3525470302/download83e6b56c5a1b576c7dd77d7799a9206eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/2b48eaff-24ca-48dd-981e-c8d1c721c6a1/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/cdfa3aaa-e3a4-4125-bce9-6f5a22d19b4e/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILPrediction of the efficiency for decision making in the agricultural sector through artificial intelligence.pdf.jpgPrediction of the efficiency for decision making in the agricultural sector 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