Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data (2023)
- Authors:
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; POPPIEL, RAUL ROBERTO - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/agriengineering5010004
- Subjects: GRANULOMETRIA DO SOLO; INTELIGÊNCIA ARTIFICIAL; MAPEAMENTO DO SOLO; REDES NEURAIS; RELEVO; TEXTURA DO SOLO
- Language: Inglês
- Imprenta:
- Source:
- Título: AgriEngineering
- ISSN: 2624-7402
- Volume/Número/Paginação/Ano: v. 5, p. 40–64, 2023
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
MALLAH, Sina et al. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, v. 5, p. 40–64, 2023Tradução . . Disponível em: https://doi.org/10.3390/agriengineering5010004. Acesso em: 09 out. 2024. -
APA
Mallah, S., Delsouz Khaki, B., Davatgar, N., Poppiel, R. R., & Demattê, J. A. M. (2023). Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, 5, 40–64. doi:10.3390/agriengineering5010004 -
NLM
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 out. 09 ] Available from: https://doi.org/10.3390/agriengineering5010004 -
Vancouver
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 out. 09 ] Available from: https://doi.org/10.3390/agriengineering5010004 - Sand fractions micromorphometry detected by Vis-NIR-MIR and its impact on water retention
- The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols
- Soil erodibility and its influential factors in the Middle East
- A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
- Bridging the gap between soil spectroscopy and traditional laboratory: insights for routine implementation
- Sand subfractions by proximal and satellite sensing: optimizing agricultural expansion in tropical sandy soils
- VNIR-SWIR Spectroscopy, XRD and traditional analyses for pedomorphogeological assessment in a tropical toposequence
- Estratificação e remoção de outliers em uma biblioteca espectral de solos, na predição de matéria orgânica do solo
- A importância das geotecnologias na caracterização do meio físico com vistas ao aumento da produtividade
- The Brazilian soil mid-infrared spectral library: the power of the fundamental range
Informações sobre o DOI: 10.3390/agriengineering5010004 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 | 3128079-Digital Mapping o... | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
