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
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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: 08 out. 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-2019-48NLM
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 out. 08 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdfVancouver
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 out. 08 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf
