Coffee-yield estimation using high-resolution time-series satellite images and machine learning (2022)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; NICOLETTI, JOÃO VÍTOR MOREIRA - ICMC ; MARTELLO, MAURÍCIO - ESALQ ; WEI, MARCELO CHAN FU - ESALQ ; CANAL FILHO, RICARDO - ESALQ
- Unidades: ESALQ; ICMC
- DOI: 10.3390/agriengineering4040057
- Subjects: AGRICULTURA DE PRECISÃO; ANÁLISE DE SÉRIES TEMPORAIS; APRENDIZADO COMPUTACIONAL; CAFÉ; IMAGEAMENTO DE SATÉLITE; SENSORIAMENTO REMOTO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: AgriEngineering
- ISSN: 2624-7402
- Volume/Número/Paginação/Ano: v. 4, p. 888-902, 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
MARTELLO, Maurício et al. Coffee-yield estimation using high-resolution time-series satellite images and machine learning. AgriEngineering, v. 4, p. 888-902, 2022Tradução . . Disponível em: https://doi.org/10.3390/agriengineering4040057. Acesso em: 27 dez. 2025. -
APA
Martello, M., Molin, J. P., Wei, M. C. F., Canal Filho, R., & Nicoletti, J. V. M. (2022). Coffee-yield estimation using high-resolution time-series satellite images and machine learning. AgriEngineering, 4, 888-902. doi:10.3390/agriengineering4040057 -
NLM
Martello M, Molin JP, Wei MCF, Canal Filho R, Nicoletti JVM. Coffee-yield estimation using high-resolution time-series satellite images and machine learning [Internet]. AgriEngineering. 2022 ; 4 888-902.[citado 2025 dez. 27 ] Available from: https://doi.org/10.3390/agriengineering4040057 -
Vancouver
Martello M, Molin JP, Wei MCF, Canal Filho R, Nicoletti JVM. Coffee-yield estimation using high-resolution time-series satellite images and machine learning [Internet]. AgriEngineering. 2022 ; 4 888-902.[citado 2025 dez. 27 ] Available from: https://doi.org/10.3390/agriengineering4040057 - Soil attributes mapping with online near-infrared spectroscopy requires spatio-temporal local calibrations
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- Challenges of digital solutions in sugarcane crop production: a review
- Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields
Informações sobre o DOI: 10.3390/agriengineering4040057 (Fonte: oaDOI API)
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