DOI:

https://doi.org/10.14483/23448393.16898

Published:

2020-10-05

Issue:

Vol. 25 No. 3 (2020): September - December

Section:

Special Section: Best Extended Articles - WEA 2015

Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop

Aplicación de un modelo de aprendizaje supervisado para analizar el comportamiento de variables medioambientales en un cultivo de café

Authors

Keywords:

Decision trees, precision agriculture, supervised learnings model, wireless sensor network (en).

Keywords:

Agricultura de precisión, arboles de decisión, modelos de aprendizaje supervisados, redes de sensores inalámbricos (es).

References

J. D. Pinto, “Monitoreo de cultivos con redes de sensores Xbee, arduino y dispositivos de medicion de suelos” Thesis, Universidad Tecnologica de Pereira, Pereira, 2015.

J. Ordieres, F. J. Martínez, A. de Pisón, A. González, F. Alba, R. Lostado, and A. V. Pernía, Redes inalámbricas de sensores: teoría y aplicación práctica. La Rioja: Universidad de la Rioja, Servicio de Publicaciones, 2009.

K. Siavosh and A. Jaramillo, “Nutricion de los cafetales en Colombia”, Avances Técnicos Cenicafé, no. 473. https://www.cenicafe.org/es/publications/AVT0473.pdf.

Portafolio, “El café vuelve a ser el motor de la economía”. [Online]. Available: https://www.portafolio.co/economia/el-cafe-vuelve-a-ser-el-motor-de-la-539712

M. Quiñones, V. González, R. Torres, and M. Jumbo, “Sistema de monitoreo de variables medioambientales usando WSN y una plataforma cloud”, Enfoque UT, vol. 7, no. 1, pp. 329-343, 2017. https://doi.org/10.29019/enfoqueute.v8n1.139

L. Ramirez, “Diseño de una arquitectura para redes de sensores con soporte para aplicaciones de deteccion de eventos”, Ph.D. Dissertation, Universidad Politecnica de Valencia, Valencia, 2012.

National Instruments, “Sensor terminilogy”. [Online]. Available: https://www.ni.com/es-co/innovations/white-papers/13/sensor-terminology.html

Colmakers, “Colmakers”. [Online]. Available: https://www.colmakers.com/

National Instruments, “Niyantra Documents”. [Online]. Available: https://forums.ni.com/t5/NIYANTRA-Documents/NIYANTRA-2013-REMOTE-MONITORING-AND-AUTOMIZED-CONTROL-SYSTEM-FOR/ta-p/3498027?profile.language=es

Ubidots, “Ubidots”. [Online]. Available: https://ubidots.com/platform/

Bigml, “Bigml”. [Online]. Available: https://bigml.com/features

L. Igual and S. Segui, Introduction to Data Science. Barcelona: Springer, 2017. https://doi.org/10.1007/978-3-319-50017-1

J. C. Garcia, H. E. Posada, and F. A. Salazar, “Factores de produccion que influyen en la respuesta de genotipos de coffea arabica L. bajo diversas condiciones ambientales en Colombia”, Cenicafe, vol. 2, no 66, pp. 30-57, 2015. https://www.cenicafe.org/es/publications/3.Factores.pdf

J. Arcila, “Factores que determinan la productividad del cafetal”, in Sistemas de produccion de cafe en Colombia. Chinchina: Cenicafe, 2007, pp. 62-86.

V. H. Ramírez, “Avances en la medicion del suelo in situ”, Investigaciones de Unisarc, vol. 4, no. 1, pp. 27-34, 2006.

A. Herron, “Producción del café en zonas no tradicionales”. [Online]. Available: https://www.urosario.

edu.co/Mision-Cafetera/Archivos/Zonas-no-tradcionales-antonio-Herron.pdf

How to Cite

APA

Ruiz Martinez, W., Ferro Escobar, R., and Moncada, J. (2020). Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop. Ingeniería, 25(3), 410–424. https://doi.org/10.14483/23448393.16898

ACM

[1]
Ruiz Martinez, W. et al. 2020. Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop. Ingeniería. 25, 3 (Oct. 2020), 410–424. DOI:https://doi.org/10.14483/23448393.16898.

ACS

(1)
Ruiz Martinez, W.; Ferro Escobar, R.; Moncada, J. Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop. Ing. 2020, 25, 410-424.

ABNT

RUIZ MARTINEZ, William; FERRO ESCOBAR, Roberto; MONCADA, Javier. Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop. Ingeniería, [S. l.], v. 25, n. 3, p. 410–424, 2020. DOI: 10.14483/23448393.16898. Disponível em: https://revistas.udistrital.edu.co/index.php/reving/article/view/16898. Acesso em: 30 dec. 2025.

Chicago

Ruiz Martinez, William, Roberto Ferro Escobar, and Javier Moncada. 2020. “Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop”. Ingeniería 25 (3):410-24. https://doi.org/10.14483/23448393.16898.

Harvard

Ruiz Martinez, W., Ferro Escobar, R. and Moncada, J. (2020) “Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop”, Ingeniería, 25(3), pp. 410–424. doi: 10.14483/23448393.16898.

IEEE

[1]
W. Ruiz Martinez, R. Ferro Escobar, and J. Moncada, “Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop”, Ing., vol. 25, no. 3, pp. 410–424, Oct. 2020.

MLA

Ruiz Martinez, William, et al. “Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop”. Ingeniería, vol. 25, no. 3, Oct. 2020, pp. 410-24, doi:10.14483/23448393.16898.

Turabian

Ruiz Martinez, William, Roberto Ferro Escobar, and Javier Moncada. “Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop”. Ingeniería 25, no. 3 (October 5, 2020): 410–424. Accessed December 30, 2025. https://revistas.udistrital.edu.co/index.php/reving/article/view/16898.

Vancouver

1.
Ruiz Martinez W, Ferro Escobar R, Moncada J. Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop. Ing. [Internet]. 2020 Oct. 5 [cited 2025 Dec. 30];25(3):410-24. Available from: https://revistas.udistrital.edu.co/index.php/reving/article/view/16898

Download Citation

Visitas

1800

Dimensions


PlumX


Downloads

Download data is not yet available.

Similar Articles

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

You may also start an advanced similarity search for this article.

Publication Facts

Metric
This article
Other articles
Peer reviewers 
3
2.4

Reviewer profiles  N/A

Author statements

Author statements
This article
Other articles
Data availability 
N/A
16%
External funding 
No
32%
Competing interests 
N/A
11%
Metric
This journal
Other journals
Articles accepted 
78%
33%
Days to publication 
39
145

Indexed in

Editor & editorial board
profiles
Loading...