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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15798
Title: A nonlinear model to estimate nitrogen level in agricultural soil using Gaussian kernels
Authors: Sánchez Mora, Katty
Zuñiga Gutierrez, María
Mayhua López, Efraín Tito
Keywords: Agriculture;Corrosion;Fertilizers;Grain (agricultural product);Nitrogen;Productivity;Soils;Agricultural productivity;Agricultural soils;Direct and indirect methods;Electrical conductivity;Environmental problems;Gaussian kernels;Nonlinear estimator;Temperature and humidities;Nitrogen fertilizers
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015147193&doi=10.1109%2fANDESCON.2016.7836247&partnerID=40&md5=f5602a71c588bdd547d69739d799ead6
Abstract: Nitrogen fertilizers are commonly used to improve agricultural productivity. However, its excessive use may cause or lead to environmental problems. Therefore, technologies capable of monitoring and measure levels of nitrogen in agricultural soil in-situ and in real time are required in order to make efficient the use of fertilizers. Nitrogen levels are usually measured by direct and indirect methods. Direct methods can be conducted in-situ or in laboratory, but they are really expensive and/or little resistant to soil conditions. Otherwise, indirect methods can estimate nitrogen levels in-situ and in real time, based on the measure of other parameters, and at the expense of accuracy. This paper proposes an indirect method to estimate the nitrogen level in agricultural soil through the measurement of the levels of electrical conductivity, temperature and humidity. The proposed model uses a nonlinear estimator based on Gaussian kernels. The results after training the model with real data showed values very close to the actual measured values. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15798
ISBN: 9781509025312
Appears in Collections:Artículos de investigación

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