ReP USP - Resultado da busca

Filtros : "Financiamento GeoCis/LSO/ESALQ/USP" "Minasny, Budiman" Limpar

Filtros

Autores USP
- dematte, jose alexandre melo (2)
- albarracin, heidy soledad rodríguez (1)
- bellinaso, henrique (1)
- gómez, andrés mauricio rico (1)
- mendes, wanderson de sousa (1)

Ano de publicação
- 2022 (~1)
- 2020 (~1)
Assuntos
- solos (2)
- análise do solo (1)
- aprendizado computacional (1)
- espectroscopia (1)
- espectroscopia infravermelha (1)

Artigo de periodico
Sensing technologies for characterizing and monitoring soil functions: a review (2022)
Silvero, Nélida Elizabet Quinõnez ; Demattê, José Alexandre Melo ; Minasny, Budiman; Rosin, Nícolas Augusto ; Nascimento, Jessica García; Albarracín, Heidy Soledad Rodríguez ; Bellinaso, Henrique ; Gómez, Andrés Mauricio Rico
Fonte: Advances in Agronomy. Unidade: ESALQ
Assuntos: ESPECTROSCOPIA, MONITORAÇÃO DOS SOLOS, SENSORIAMENTO REMOTO, SOLOS, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVERO, Nélida Elizabet Quinõnez et al. Sensing technologies for characterizing and monitoring soil functions: a review. Advances in Agronomy, p. 1-44, 2022Tradução . . Disponível em: https://doi.org/10.1016/bs.agron.2022.08.002. Acesso em: 17 nov. 2025.
APA
Silvero, N. E. Q., Demattê, J. A. M., Minasny, B., Rosin, N. A., Nascimento, J. G., Albarracín, H. S. R., et al. (2022). Sensing technologies for characterizing and monitoring soil functions: a review. Advances in Agronomy, 1-44. doi:10.1016/bs.agron.2022.08.002
NLM
Silvero NEQ, Demattê JAM, Minasny B, Rosin NA, Nascimento JG, Albarracín HSR, Bellinaso H, Gómez AMR. Sensing technologies for characterizing and monitoring soil functions: a review [Internet]. Advances in Agronomy. 2022 ; 1-44.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1016/bs.agron.2022.08.002
Vancouver
Silvero NEQ, Demattê JAM, Minasny B, Rosin NA, Nascimento JG, Albarracín HSR, Bellinaso H, Gómez AMR. Sensing technologies for characterizing and monitoring soil functions: a review [Internet]. Advances in Agronomy. 2022 ; 1-44.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1016/bs.agron.2022.08.002
Artigo de periodico
Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning (2020)
Ng, Wartini; Minasny, Budiman; Mendes, Wanderson de Sousa ; Dematte, Jose Alexandre Melo
Fonte: Soil Discussions. Unidade: ESALQ
Assuntos: ANÁLISE DO SOLO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, REDES NEURAIS, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NG, Wartini et al. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, v. 6, p. 565–578, 2020Tradução . . Disponível em: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf. Acesso em: 17 nov. 2025.
APA
Ng, W., Minasny, B., Mendes, W. de S., & Dematte, J. A. M. (2020). Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, 6, 565–578. doi:10.5194/soil-2019-48
NLM
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2020 ; 6 565–578.[citado 2025 nov. 17 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf
Vancouver
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2020 ; 6 565–578.[citado 2025 nov. 17 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo 2012 - 2025