Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN (2022)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; SOUSA, RAFAEL VIEIRA DE - FZEA ; DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; TAVARES, TIAGO RODRIGUES - CENA
- Unidades: ESALQ; FZEA; CENA
- DOI: 10.3390/automation3010006
- Subjects: AGRICULTURA DE PRECISÃO; ESPECTROSCOPIA INFRAVERMELHA; FERTILIDADE DO SOLO; MAPEAMENTO DO SOLO; MODELAGEM DE DADOS; SENSOR; SOLO TROPICAL
- Keywords: Sensoriamento proximal do solo
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Automation
- ISSN: 2673-4052
- Volume/Número/Paginação/Ano: v. 3, p. 116-131, February 2022
- 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
EITELWEIN, Mateus Tonini et al. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN. Automation, v. 3, p. 116-131, 2022Tradução . . Disponível em: https://doi.org/10.3390/automation3010006. Acesso em: 04 out. 2024. -
APA
Eitelwein, M. T., Tavares, T. R., Molin, J. P., Trevisan, R. G., Sousa, R. V. de, & Demattê, J. A. M. (2022). Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN. Automation, 3, 116-131. doi:10.3390/automation3010006 -
NLM
Eitelwein MT, Tavares TR, Molin JP, Trevisan RG, Sousa RV de, Demattê JAM. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN [Internet]. Automation. 2022 ; 3 116-131.[citado 2024 out. 04 ] Available from: https://doi.org/10.3390/automation3010006 -
Vancouver
Eitelwein MT, Tavares TR, Molin JP, Trevisan RG, Sousa RV de, Demattê JAM. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN [Internet]. Automation. 2022 ; 3 116-131.[citado 2024 out. 04 ] Available from: https://doi.org/10.3390/automation3010006 - Cotton nitrogen uptake estimation for in-season fertilizer management based on proximal digital image analysis
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Informações sobre o DOI: 10.3390/automation3010006 (Fonte: oaDOI API)
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