Preprint Article Version 1 This version is not peer-reviewed

Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils

Version 1 : Received: 27 October 2018 / Approved: 29 October 2018 / Online: 29 October 2018 (05:17:56 CET)

A peer-reviewed article of this Preprint also exists.

Landeta-Escamilla, O.; Sandoval-Gonzalez, O.; Martínez-Sibaja, A.; Flores-Cuautle, J.J.A.; Posada-Gómez, R.; Alvarado-Lassman, A. Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils. Sensors 2019, 19, 240. Landeta-Escamilla, O.; Sandoval-Gonzalez, O.; Martínez-Sibaja, A.; Flores-Cuautle, J.J.A.; Posada-Gómez, R.; Alvarado-Lassman, A. Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils. Sensors 2019, 19, 240.

Journal reference: Sensors 2019, 19, 240
DOI: 10.3390/s19020240

Abstract

Soil conditions is a major aspect of interest for farmers due to the knowing of the physicochemical properties of the same can help with any necessary restoration of soil that guarantees the quality and the production of their crop. However, technology and analysis of the soil become of difficult access mainly in developing countries, by which the present paper shows the development of a system thought to estimate physicochemical variables of soils growing sugar cane through studies of spectroscopy. Its characteristic is that it is a portable system, with low cost, easy to use and can estimate physicochemical variables in-situ with the objective of knowing the degree of degradation present in the soil and through this help the farmers define possible strategies to restore it. The device uses the frequency response of the soil determining values of magnitude and phase, which are used by algorithms of artificial intelligence capable of getting an estimation of the physicochemical properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil at laboratories.

Subject Areas

artificial intelligence; electronics; psychochemical properties; frequency response of soil

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