Machine Learning and Feature Selection for soil spectroscopy. An evaluation of Random Forest wrappers to predict soil organic matter, clay, and carbonates-Reference-Cited by-同舟云学术

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Machine Learning and Feature Selection for soil spectroscopy. An evaluation of Random Forest wrappers to predict soil organic matter, clay, and carbonates

Published:2024-05 Issue:9 Volume:10 Page:e30228
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Canero Francisco M.^ORCID,Rodriguez-Galiano Victor,Aragones David

Funder

Ministerio de Asuntos Economicos y Transformacion Digital

Gobierno de España Ministerio de Universidades

España Ministerio para la Transición Ecológica

Publisher

Elsevier BV

Reference111 articles.

1. Modern Spectroscopy;Hollas,2004

2. Prediction of soil parameters using the spectral range between 350 and 15,000 nm: a case study based on the Permanent Soil Monitoring Program in Saxony, Germany;Riedel;Geoderma,2018

3. Reflectance spectroscopy study of Cd contamination in the sediments of the changjiang river, China;Xia;Environ. Sci. Technol.,2007

4. Near-infrared reflectance spectroscopic analysis of soil C and N;Chang;Soil Sci.,2002

5. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement;McCarty;Soil Sci. Soc. Am. J.,2002

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