Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil (2024)
Source: Sugar Tech. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO
ABNT
AMARO, Rafaella Pironato et al. Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil. Sugar Tech, p. 1-11, 2024Tradução . . Disponível em: https://doi.org/10.1007/s12355-024-01468-z. Acesso em: 02 nov. 2024.APA
Amaro, R. P., Christina, M., Todoroff, P., Le Maire, G., Fiorio, P. R., Pereira, E. de C., & Luciano, A. C. dos S. (2024). Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil. Sugar Tech, 1-11. doi:10.1007/s12355-024-01468-zNLM
Amaro RP, Christina M, Todoroff P, Le Maire G, Fiorio PR, Pereira E de C, Luciano AC dos S. Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil [Internet]. Sugar Tech. 2024 ; 1-11.[citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/s12355-024-01468-zVancouver
Amaro RP, Christina M, Todoroff P, Le Maire G, Fiorio PR, Pereira E de C, Luciano AC dos S. Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil [Internet]. Sugar Tech. 2024 ; 1-11.[citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/s12355-024-01468-z