Journal article
Ofelia Landeta-Escamilla, O. Sandoval-González, A. Martínez-Sibaja, J. J. A. Flores-Cuautle, R. Posada-Gómez, A. Alvarado-Lassman
2018
2018
Semantic Scholar
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Cite
APA
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Landeta-Escamilla, O., Sandoval-González, O., Martínez-Sibaja, A., Flores-Cuautle, J. J. A., Posada-Gómez, R., & Alvarado-Lassman, A. (2018). 1 Intelligent spectroscopy system used for 2 physicochemical variables estimation in sugar 3 cane soils 4.
Chicago/Turabian
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Landeta-Escamilla, Ofelia, O. Sandoval-González, A. Martínez-Sibaja, J. J. A. Flores-Cuautle, R. Posada-Gómez, and A. Alvarado-Lassman. “1 Intelligent Spectroscopy System Used for 2 Physicochemical Variables Estimation in Sugar 3 Cane Soils 4” (2018).
MLA
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Landeta-Escamilla, Ofelia, et al. 1 Intelligent Spectroscopy System Used for 2 Physicochemical Variables Estimation in Sugar 3 Cane Soils 4. 2018.
BibTeX Click to copy
@article{ofelia2018a,
title = {1 Intelligent spectroscopy system used for 2 physicochemical variables estimation in sugar 3 cane soils 4},
year = {2018},
author = {Landeta-Escamilla, Ofelia and Sandoval-González, O. and Martínez-Sibaja, A. and Flores-Cuautle, J. J. A. and Posada-Gómez, R. and Alvarado-Lassman, A.}
}
Abstract
Soil conditions is a major aspect of interest for farmers due to the knowing of the 11 physicochemical properties of the same can help with any necessary restoration of soil that 12 guarantees the quality and the production of their crop. However, technology and analysis of the 13 soil become of difficult access mainly in developing countries, by which the present paper shows 14 the development of a system thought to estimate physicochemical variables of soils growing sugar 15 cane through studies of spectroscopy. Its characteristic is that it is a portable system, with low cost, 16 easy to use and can estimate physicochemical variables in-situ with the objective of knowing the 17 degree of degradation present in the soil and through this help the farmers define possible strategies 18 to restore it. The device uses the frequency response of the soil determining values of magnitude 19 and phase, which are used by algorithms of artificial intelligence capable of getting an estimation of 20 the physicochemical properties. The obtained results show errors below 8% in the estimation of the 21 variables compared to the analysis results of the soil at laboratories. 22
