3D data processing to characterize the spatial variability of sugarcane fields (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; CANATA, TATIANA FERNANDA - ESALQ ; MARTELLO, MAURÍCIO - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ
- Unidade: ESALQ
- DOI: 10.1007/s12355-021-01048-5
- Subjects: AGRICULTURA DE PRECISÃO; CANA-DE-AÇÚCAR; IMAGEM 3D; PROCESSAMENTO DE DADOS; SENSORIAMENTO REMOTO; TECNOLOGIA LIDAR; VARIABILIDADE ESPACIAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Sugar Tech
- ISSN: 0972-1525
- Volume/Número/Paginação/Ano: online, September 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CANATA, Tatiana Fernanda et al. 3D data processing to characterize the spatial variability of sugarcane fields. Sugar Tech, 2021Tradução . . Disponível em: https://doi.org/10.1007/s12355-021-01048-5. Acesso em: 27 abr. 2024. -
APA
Canata, T. F., Martello, M., Maldaner, L. F., Moreira, J. de S., & Molin, J. P. (2021). 3D data processing to characterize the spatial variability of sugarcane fields. Sugar Tech. doi:10.1007/s12355-021-01048-5 -
NLM
Canata TF, Martello M, Maldaner LF, Moreira J de S, Molin JP. 3D data processing to characterize the spatial variability of sugarcane fields [Internet]. Sugar Tech. 2021 ;[citado 2024 abr. 27 ] Available from: https://doi.org/10.1007/s12355-021-01048-5 -
Vancouver
Canata TF, Martello M, Maldaner LF, Moreira J de S, Molin JP. 3D data processing to characterize the spatial variability of sugarcane fields [Internet]. Sugar Tech. 2021 ;[citado 2024 abr. 27 ] Available from: https://doi.org/10.1007/s12355-021-01048-5 - A system for plant detection using sensor fusion approach based on machine learning model
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- High-resolution imagery data to assess the spatial variability of sugarcane fields
- A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations
- Methodology to filter out outliers in high spatial density data to improve maps reliability
- Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches
- Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester
- Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives
Informações sobre o DOI: 10.1007/s12355-021-01048-5 (Fonte: oaDOI API)
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