Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil (2025)
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, v. 27, n. 1, p. 108–118, 2025Tradução . . Disponível em: https://doi.org/10.1007/s12355-024-01468-z. Acesso em: 01 dez. 2025.APA
Amaro, R. P., Christina, M., Todoroff, P., Le Maire, G., Fiorio, P. R., Pereira, E. de C., & Luciano, A. C. dos S. (2025). Regional model to predict sugarcane yield using sentinel-2 imagery in São Paulo State, Brazil. Sugar Tech, 27( 1), 108–118. 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. 2025 ; 27( 1): 108–118.[citado 2025 dez. 01 ] 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. 2025 ; 27( 1): 108–118.[citado 2025 dez. 01 ] Available from: https://doi.org/10.1007/s12355-024-01468-z
