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Cited by 54Soil Geostatistics and MappingNeural Networks and ApplicationsSmart Agriculture and AI

Estimation of soil organic matter content by modeling with artificial neural networks

Open access
Fernandes, Mariele Monique Honorato, Coelho, Anderson Prates, Fernandes, Carolina, Silva, Matheus Flavio da, Dela Marta, Claudia Campos·2019·Geoderma
DOI 10.1016/j.geoderma.2019.04.044
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Crossref · 10.1016/j.geoderma.2019.04.044

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Authors
Fernandes, M. M. H., Coelho, A. P., Fernandes, C., Silva, M. F. D., & Dela Marta, C. C.
Affiliations
Universidade Estadual Paulista (Unesp) (BR)
Corresponding author
Coelho, Anderson Prates
ORCID
Venue
Geoderma
Publisher
Elsevier BV
Published
2019-09
Volume
350
Pages
46-51
ISSN
0016-7061 (ISSN-L)
DOI
10.1016/j.geoderma.2019.04.044
OpenAlex
W2945015108
Cited by
54
References
0
Citations by year
2026: 5 · 2025: 9 · 2024: 7 · 2023: 11 · 2022: 9 · 2021: 7 · 2020: 5 · 2019: 1
FWCI
2.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Geoderma (publishedVersion)
  • Americanae (AECID Library) (submittedVersion, other-oa)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
Funders
  • Conselho Nacional de Desenvolvimento Científico e Tecnológic
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Geostatistics and Mapping
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
soil organic matter estimation, artificial neural networks, multilayer perceptron, soil chemical properties, soil pH, soil acidity, network complexity, prediction accuracy, soil fertility

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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