Soil mineralogical attributes estimated by color as accessed by proximal sensors and machine learning-Reference-Cited by-同舟云学术

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Soil mineralogical attributes estimated by color as accessed by proximal sensors and machine learning

Published:2021-09-09 Issue:6 Volume:85 Page:2185-2200
ISSN:0361-5995
Container-title:Soil Science Society of America Journal
language:en
Short-container-title:Soil Sci. Soc. Am. j.

Author:

Baldo Danilo¹^ORCID,Marques José¹^ORCID,Fernandes Kathleen¹^ORCID,Almeida Gabriela Mourão¹^ORCID,Siqueira Diego Silva¹^ORCID

Affiliation:

1. Dep. of Agriculture Sciences, Research Group CSME—Soil Characterization for Specific Management Faculty of Agrarian and Veterinary Sciences, São Paulo State Univ. (FCAV/UNESP) Jaboticabal São Paulo Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Wiley

Subject

Soil Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/saj2.20309

Reference84 articles.

1. Digital RGB photography and visible-range spectroscopy for soil composition analysis

2. Procedures using diffuse reflectance spectroscopy for estimating hematite and goethite in Oxisols of São Paulo, Brazil

3. Using machine learning to identify the geographical drivers of Ceratitis capitata trap catch in an agricultural landscape

4. Mineralogy of the clay fraction of alfisols in two slope curvatures;Camargo L. A.;Revista Brasileira de Ciência do Solo,2013

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