Araújo, Suzana Romeiro 
; Wetterlind, J; Demattê, José Alexandre Melo ; Stenberg, B
Fonte: European Journal of Soil Science. Unidade: ESALQ
Assuntos: ARGILAS, ESPECTROSCOPIA INFRAVERMELHA, MATÉRIA ORGÂNICA DO SOLO, MINERAÇÃO DE DADOS, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARAÚJO, Suzana Romeiro et al. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. European Journal of Soil Science, v. 65, p. 718–729, 2014Tradução . . Disponível em: https://doi.org/10.1111/ejss.12165. Acesso em: 09 maio 2024.APA
Araújo, S. R., Wetterlind, J., Demattê, J. A. M., & Stenberg, B. (2014). Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. European Journal of Soil Science, 65, 718–729. doi:10.1111/ejss.12165NLM
Araújo SR, Wetterlind J, Demattê JAM, Stenberg B. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques [Internet]. European Journal of Soil Science. 2014 ; 65 718–729.[citado 2024 maio 09 ] Available from: https://doi.org/10.1111/ejss.12165Vancouver
Araújo SR, Wetterlind J, Demattê JAM, Stenberg B. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques [Internet]. European Journal of Soil Science. 2014 ; 65 718–729.[citado 2024 maio 09 ] Available from: https://doi.org/10.1111/ejss.12165
