Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; CARVALHO, HUDSON WALLACE PEREIRA DE - CENA ; TAVARES, TIAGO RODRIGUES - ESALQ
- Unidades: ESALQ; CENA
- DOI: 10.3390/s21010148
- Subjects: AGRICULTURA DE PRECISÃO; ESPECTROSCOPIA INFRAVERMELHA; FERTILIDADE DO SOLO; FLUORESCÊNCIA; RAIOS X; SENSOR; SOLO TROPICAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
TAVARES, Tiago Rodrigues et al. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches. Sensors, v. 21, p. 493-501, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21010148. Acesso em: 18 abr. 2024. -
APA
Tavares, T. R., Molin, J. P., Javadi, S. H., Carvalho, H. W. P. de, & Mouazen, A. M. (2021). Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches. Sensors, 21, 493-501. doi:10.3390/s21010148 -
NLM
Tavares TR, Molin JP, Javadi SH, Carvalho HWP de, Mouazen AM. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches [Internet]. Sensors. 2021 ; 21 493-501.[citado 2024 abr. 18 ] Available from: https://doi.org/10.3390/s21010148 -
Vancouver
Tavares TR, Molin JP, Javadi SH, Carvalho HWP de, Mouazen AM. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches [Internet]. Sensors. 2021 ; 21 493-501.[citado 2024 abr. 18 ] Available from: https://doi.org/10.3390/s21010148 - Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction
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- Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN
Informações sobre o DOI: 10.3390/s21010148 (Fonte: oaDOI API)
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