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Artigo de periodico
Hydro-pedotransfer functions: a roadmap for future development (2024)
Weber, Tobias Karl David ; Weihermüller, Lutz ; Nemes, Attila ; Bechtold, Michel ; Degré, Aurore ; Diamantopoulos, Efstathios ; Fatichi, Simone ; Filipović, Vilim ; Gupta, Surya ; Hohenbrink, Tobias L ; Hirmas, Daniel R ; Jackisch, Conrad ; De Jong Van Lier, Quirijn ; Koestel, John ; Lehmann, Peter ; Marthews, Toby R ; Minasny, Budiman ; Pagel, Holger ; van der Ploeg, Martine ; Shojaeezadeh, Shahab Aldin ; Svane, Simon Fiil ; Szabó, Brigitta ; Vereecken, Harry ; Verhoef, Anne ; Young, Michael ; Zeng, Yijian ; Zhang, Yonggen ; Bonetti, Sara
Fonte: Hydrology and Earth System Sciences. Unidade: CENA
Assuntos: FÍSICA DO SOLO, HIDROLOGIA, PEDOLOGIA, SOLOS, AGRICULTURA, HIDRÁULICA APLICADA
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ABNT
WEBER, Tobias Karl David et al. Hydro-pedotransfer functions: a roadmap for future development. Hydrology and Earth System Sciences, v. 28, p. 3391-3433, 2024Tradução . . Disponível em: https://doi.org/10.5194/hess-28-3391-2024. Acesso em: 20 set. 2024.
APA
Weber, T. K. D., Weihermüller, L., Nemes, A., Bechtold, M., Degré, A., Diamantopoulos, E., et al. (2024). Hydro-pedotransfer functions: a roadmap for future development. Hydrology and Earth System Sciences, 28, 3391-3433. doi:10.5194/hess-28-3391-2024
NLM
Weber TKD, Weihermüller L, Nemes A, Bechtold M, Degré A, Diamantopoulos E, Fatichi S, Filipović V, Gupta S, Hohenbrink TL, Hirmas DR, Jackisch C, De Jong Van Lier Q, Koestel J, Lehmann P, Marthews TR, Minasny B, Pagel H, van der Ploeg M, Shojaeezadeh SA, Svane SF, Szabó B, Vereecken H, Verhoef A, Young M, Zeng Y, Zhang Y, Bonetti S. Hydro-pedotransfer functions: a roadmap for future development [Internet]. Hydrology and Earth System Sciences. 2024 ; 28 3391-3433.[citado 2024 set. 20 ] Available from: https://doi.org/10.5194/hess-28-3391-2024
Vancouver
Weber TKD, Weihermüller L, Nemes A, Bechtold M, Degré A, Diamantopoulos E, Fatichi S, Filipović V, Gupta S, Hohenbrink TL, Hirmas DR, Jackisch C, De Jong Van Lier Q, Koestel J, Lehmann P, Marthews TR, Minasny B, Pagel H, van der Ploeg M, Shojaeezadeh SA, Svane SF, Szabó B, Vereecken H, Verhoef A, Young M, Zeng Y, Zhang Y, Bonetti S. Hydro-pedotransfer functions: a roadmap for future development [Internet]. Hydrology and Earth System Sciences. 2024 ; 28 3391-3433.[citado 2024 set. 20 ] Available from: https://doi.org/10.5194/hess-28-3391-2024
Artigo de periodico
Remote sensing of the Earth’s soil color in space and time (2023)
Rizzo, Rodnei ; Wadoux, Alexandre M. J; Demattê, José Alexandre Melo ; Minasny, Budiman ; Barrón, Vidal; Ben-Dor, Eyal ; Francos, Nicolas ; Savin, Igor; Poppiel, Raul Roberto ; Silvero, Nelida Elizabet Quiñonez ; Terra, Fabrício da Silva; Rosin, Nícolas Augusto ; Rosas, Jorge Tadeu Fim ; Greschuk, Lucas Tadeu ; Ballester, Maria Victoria Ramos ; Gómez, Andrés Mauricio Rico ; Belllinaso, Henrique; Safanelli, José Lucas ; Chabrillat, Sabine ; Fiorio, Peterson Roberto ; Das, Bhabani Sankar ; Malone, Brendan P; Zalidis, George; Tziolas, Nikolaos ; Tsakiridis, Nikolaos; Karyotis, Konstantinos; Samarinas, Nikiforos; Kalopesa, Eleni; Gholizadeh, Asa ; Shepherd, Keith D; Milewski, Robert; Vaudour, Emmanuelle; Wang, Changkun; Salama, Elsayed Said Mohamed
Fonte: Remote Sensing of Environment. Unidades: ESALQ, CENA
Assuntos: COR DO SOLO, ESPECTROSCOPIA, IMAGEAMENTO DE SATÉLITE, MONITORAÇÃO DOS SOLOS, SENSORIAMENTO REMOTO
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ABNT
RIZZO, Rodnei et al. Remote sensing of the Earth’s soil color in space and time. Remote Sensing of Environment, v. 299, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2023.113845. Acesso em: 20 set. 2024.
APA
Rizzo, R., Wadoux, A. M. J., Demattê, J. A. M., Minasny, B., Barrón, V., Ben-Dor, E., et al. (2023). Remote sensing of the Earth’s soil color in space and time. Remote Sensing of Environment, 299, 1-15. doi:10.1016/j.rse.2023.113845
NLM
Rizzo R, Wadoux AMJ, Demattê JAM, Minasny B, Barrón V, Ben-Dor E, Francos N, Savin I, Poppiel RR, Silvero NEQ, Terra F da S, Rosin NA, Rosas JTF, Greschuk LT, Ballester MVR, Gómez AMR, Belllinaso H, Safanelli JL, Chabrillat S, Fiorio PR, Das BS, Malone BP, Zalidis G, Tziolas N, Tsakiridis N, Karyotis K, Samarinas N, Kalopesa E, Gholizadeh A, Shepherd KD, Milewski R, Vaudour E, Wang C, Salama ESM. Remote sensing of the Earth’s soil color in space and time [Internet]. Remote Sensing of Environment. 2023 ; 299 1-15.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.rse.2023.113845
Vancouver
Rizzo R, Wadoux AMJ, Demattê JAM, Minasny B, Barrón V, Ben-Dor E, Francos N, Savin I, Poppiel RR, Silvero NEQ, Terra F da S, Rosin NA, Rosas JTF, Greschuk LT, Ballester MVR, Gómez AMR, Belllinaso H, Safanelli JL, Chabrillat S, Fiorio PR, Das BS, Malone BP, Zalidis G, Tziolas N, Tsakiridis N, Karyotis K, Samarinas N, Kalopesa E, Gholizadeh A, Shepherd KD, Milewski R, Vaudour E, Wang C, Salama ESM. Remote sensing of the Earth’s soil color in space and time [Internet]. Remote Sensing of Environment. 2023 ; 299 1-15.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.rse.2023.113845
Artigo de periodico
Incorporating soil knowledge into machine‐learning prediction of soil properties from soil spectra (2023)
Ma, Yuxin; Minasny, Budiman ; Demattê, José Alexandre Melo ; McBratney, Alex B
Fonte: European Journal of Soil Science. Unidade: ESALQ
Assuntos: APRENDIZADO COMPUTACIONAL, CARBONO, ESPECTROSCOPIA INFRAVERMELHA, SOLOS
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ABNT
MA, Yuxin et al. Incorporating soil knowledge into machine‐learning prediction of soil properties from soil spectra. European Journal of Soil Science, v. 74, p. 1-13, 2023Tradução . . Disponível em: https://doi.org/10.1111/ejss.13438. Acesso em: 20 set. 2024.
APA
Ma, Y., Minasny, B., Demattê, J. A. M., & McBratney, A. B. (2023). Incorporating soil knowledge into machine‐learning prediction of soil properties from soil spectra. European Journal of Soil Science, 74, 1-13. doi:10.1111/ejss.13438
NLM
Ma Y, Minasny B, Demattê JAM, McBratney AB. Incorporating soil knowledge into machine‐learning prediction of soil properties from soil spectra [Internet]. European Journal of Soil Science. 2023 ; 74 1-13.[citado 2024 set. 20 ] Available from: https://doi.org/10.1111/ejss.13438
Vancouver
Ma Y, Minasny B, Demattê JAM, McBratney AB. Incorporating soil knowledge into machine‐learning prediction of soil properties from soil spectra [Internet]. European Journal of Soil Science. 2023 ; 74 1-13.[citado 2024 set. 20 ] Available from: https://doi.org/10.1111/ejss.13438
Artigo de periodico
Estimating plant-available nutrients with XRF sensors: towards a versatile analysis tool for soil condition assessment (2023)
Tavares, Tiago Rodrigues ; Minasny, Budiman ; McBratney, Alex; Cherubin, Maurício Roberto ; Marques, Gabriel Toledo; Ragagnin, Marcos Mantelli ; Alves, Elton Eduardo Novais ; Padarian, José; Lavres, José ; Carvalho, Hudson Wallace Pereira de
Fonte: Geoderma. Unidades: ESALQ, CENA
Assuntos: ANÁLISE DO SOLO, ESPECTROSCOPIA, NUTRIENTES MINERAIS DO SOLO, QUALIDADE DO SOLO, QUIMIOMETRIA, SENSOR
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ABNT
TAVARES, Tiago Rodrigues et al. Estimating plant-available nutrients with XRF sensors: towards a versatile analysis tool for soil condition assessment. Geoderma, v. 439, p. 1-10, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.geoderma.2023.116701. Acesso em: 20 set. 2024.
APA
Tavares, T. R., Minasny, B., McBratney, A., Cherubin, M. R., Marques, G. T., Ragagnin, M. M., et al. (2023). Estimating plant-available nutrients with XRF sensors: towards a versatile analysis tool for soil condition assessment. Geoderma, 439, 1-10. doi:10.1016/j.geoderma.2023.116701
NLM
Tavares TR, Minasny B, McBratney A, Cherubin MR, Marques GT, Ragagnin MM, Alves EEN, Padarian J, Lavres J, Carvalho HWP de. Estimating plant-available nutrients with XRF sensors: towards a versatile analysis tool for soil condition assessment [Internet]. Geoderma. 2023 ; 439 1-10.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116701
Vancouver
Tavares TR, Minasny B, McBratney A, Cherubin MR, Marques GT, Ragagnin MM, Alves EEN, Padarian J, Lavres J, Carvalho HWP de. Estimating plant-available nutrients with XRF sensors: towards a versatile analysis tool for soil condition assessment [Internet]. Geoderma. 2023 ; 439 1-10.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116701
Artigo de periodico
Mapping Brazilian soil mineralogy using proximal and remote sensing data (2023)
Rosin, Nícolas Augusto ; Demattê, José Alexandre Melo ; Poppiel, Raul Roberto ; Silvero, Nélida E. Q ; Rodriguez-Albarracin, Heidy S ; Rosas, Jorge Tadeu Fim ; Greschuk, Lucas Tadeu ; Bellinaso, Henrique; Minasny, Budiman; Gomez, Cecile; Marques Júnior, José; Fernandes, Kathleen
Fonte: Geoderma. Unidade: ESALQ
Assuntos: ESPECTROSCOPIA, MAPEAMENTO DO SOLO, MINERALOGIA DO SOLO, SENSOR, SENSORIAMENTO REMOTO
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ABNT
ROSIN, Nícolas Augusto et al. Mapping Brazilian soil mineralogy using proximal and remote sensing data. Geoderma, v. 432, p. 1-19, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.geoderma.2023.116413. Acesso em: 20 set. 2024.
APA
Rosin, N. A., Demattê, J. A. M., Poppiel, R. R., Silvero, N. E. Q., Rodriguez-Albarracin, H. S., Rosas, J. T. F., et al. (2023). Mapping Brazilian soil mineralogy using proximal and remote sensing data. Geoderma, 432, 1-19. doi:10.1016/j.geoderma.2023.116413
NLM
Rosin NA, Demattê JAM, Poppiel RR, Silvero NEQ, Rodriguez-Albarracin HS, Rosas JTF, Greschuk LT, Bellinaso H, Minasny B, Gomez C, Marques Júnior J, Fernandes K. Mapping Brazilian soil mineralogy using proximal and remote sensing data [Internet]. Geoderma. 2023 ; 432 1-19.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116413
Vancouver
Rosin NA, Demattê JAM, Poppiel RR, Silvero NEQ, Rodriguez-Albarracin HS, Rosas JTF, Greschuk LT, Bellinaso H, Minasny B, Gomez C, Marques Júnior J, Fernandes K. Mapping Brazilian soil mineralogy using proximal and remote sensing data [Internet]. Geoderma. 2023 ; 432 1-19.[citado 2024 set. 20 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116413
Artigo de periodico
The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah ; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida E. Q
Fonte: Remote Sensing. Unidade: ESALQ
Assuntos: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, SOLOS
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ABNT
DEMATTÊ, José Alexandre Melo et al. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, v. 14, p. 1-27, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14030740. Acesso em: 20 set. 2024.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, 14, 1-27. doi:10.3390/rs14030740
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2024 set. 20 ] Available from: https://doi.org/10.3390/rs14030740
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2024 set. 20 ] Available from: https://doi.org/10.3390/rs14030740
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
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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: 20 set. 2024.
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 2024 set. 20 ] 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 2024 set. 20 ] Available from: https://doi.org/10.1016/bs.agron.2022.08.002
Artigo de periodico
Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah ; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida Elizabet Quiñonez
Fonte: Remote Sensing. Unidade: ESALQ
Assuntos: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, QUALIDADE DO SOLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DEMATTÊ, José Alexandre Melo et al. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, v. 14, p. 1-2, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14061459. Acesso em: 20 set. 2024.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, 14, 1-2. doi:10.3390/rs14061459
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2024 set. 20 ] Available from: https://doi.org/10.3390/rs14061459
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2024 set. 20 ] Available from: https://doi.org/10.3390/rs14061459
Artigo de periodico
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)
Ng, Wartini; Minasny, Budiman; Mendes, Wanderson de Sousa ; Demattê, Jose Alexandre Melo
Fonte: Soil. Unidade: ESALQ
Assuntos: APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, MODELOS MATEMÁTICOS, REDES NEURAIS, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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://doi.org/10.5194/soil-6-565-2020. Acesso em: 20 set. 2024.
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 2024 set. 20 ] Available from: https://doi.org/10.5194/soil-6-565-2020
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 2024 set. 20 ] Available from: https://doi.org/10.5194/soil-6-565-2020
Artigo de periodico
Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning (2019)
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, p. 1-21 (pré-print), 2019Tradução . . Disponível em: https://doi.org/10.5194/soil-2019-48. Acesso em: 20 set. 2024.
APA
Ng, W., Minasny, B., Mendes, W. de S., & Dematte, J. A. M. (2019). Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, 1-21 (pré-print). 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. 2019 ; 1-21 (pré-print).[citado 2024 set. 20 ] Available from: https://doi.org/10.5194/soil-2019-48
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. 2019 ; 1-21 (pré-print).[citado 2024 set. 20 ] Available from: https://doi.org/10.5194/soil-2019-48

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