Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning (2020)
- Authors:
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; MENDES, WANDERSON DE SOUSA - ESALQ
- Unidade: ESALQ
- DOI: 10.5194/soil-2019-48
- Subjects: ANÁLISE DO SOLO; APRENDIZADO COMPUTACIONAL; ESPECTROSCOPIA INFRAVERMELHA; REDES NEURAIS; SOLOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Soil Discussions
- ISSN: 2199-3998
- Volume/Número/Paginação/Ano: v. 6, p. 565–578, 2020
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão aceita (Póst-print)
- Acessar versão aberta:
-
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: 29 mar. 2026. -
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 2026 mar. 29 ] 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 2026 mar. 29 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf - The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data
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