Prediction of the efficiency for decision making in the agricultural sector through artificial intelligence

Thumbnail
View/Open
Date
2021
Author
Viloria, Amelec
Ruiz Lázaro, Alex
Echeverría González, Ana Maria
Pineda Lezama, Omar Bonerge
Lamby Barrios, Juan Guillermo
Leon Castro, Nadia
Metadata
Show full item record
Compartir:


Impacto

URI: https://hdl.handle.net/11323/7711
Citar con DOI: https://doi.org/10.1007/978-981-15-7234-0_91

Enlace externo del documento: https://link.springer.com/chapter/10.1007/978-981-15-7234-0_91

Abstract

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
Collections

The following license files are associated with this item:

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