Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; CANATA, TATIANA FERNANDA - ESALQ ; WEI, MARCELO CHAN FU - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/rs13020232
- Subjects: AGRICULTURA DE PRECISÃO; APRENDIZADO COMPUTACIONAL; CANA-DE-AÇÚCAR; IMAGEAMENTO DE SATÉLITE; SENSORIAMENTO REMOTO
- Keywords: Índice de vegetação
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Remote Sensing
- ISSN: 2072-4292
- Volume/Número/Paginação/Ano: v. 13, art. 232, p. 1-14, January 2021
- 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
CANATA, Tatiana Fernanda et al. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, v. 13, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13020232. Acesso em: 27 dez. 2025. -
APA
Canata, T. F., Wei, M. C. F., Maldaner, L. F., & Molin, J. P. (2021). Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, 13, 1-14. doi:10.3390/rs13020232 -
NLM
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2025 dez. 27 ] Available from: https://doi.org/10.3390/rs13020232 -
Vancouver
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2025 dez. 27 ] Available from: https://doi.org/10.3390/rs13020232 - Carrot yield mapping: a precision agriculture approach based on machine learning
- Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches
- A system for plant detection using sensor fusion approach based on machine learning model
- High-resolution imagery data to assess the spatial variability of sugarcane fields
- 3D data processing to characterize 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
- High-resolution yield mapping for Eucalyptus grandis: a case study
- Soybean Yield Estimation and Its Components: A Linear Regression Approach
- Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester
Informações sobre o DOI: 10.3390/rs13020232 (Fonte: oaDOI API)
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