The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data (2020)
- Authors:
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; MENDES, WANDERSON DE SOUSA - ESALQ
- Unidade: ESALQ
- DOI: 10.5194/soil-6-565-2020
- Subjects: APRENDIZADO COMPUTACIONAL; ESPECTROSCOPIA INFRAVERMELHA; MODELOS MATEMÁTICOS; REDES NEURAIS; SOLOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
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ABNT
NG, Wartini et al. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. Soil, v. 6, n. 2, p. 565-578, 2020Tradução . . Disponível em: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf. Acesso em: 30 mar. 2026. -
APA
Ng, W., Minasny, B., Mendes, W. de S., & Demattê, J. A. M. (2020). The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. Soil, 6( 2), 565-578. doi:10.5194/soil-6-565-2020 -
NLM
Ng W, Minasny B, Mendes W de S, Demattê JAM. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data [Internet]. Soil. 2020 ; 6( 2): 565-578.[citado 2026 mar. 30 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf -
Vancouver
Ng W, Minasny B, Mendes W de S, Demattê JAM. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data [Internet]. Soil. 2020 ; 6( 2): 565-578.[citado 2026 mar. 30 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf - Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning
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