Classification of sugarcane areas in Landsat images using machine learning algorithms (2024)
Fonte: Brazilian Journal of Biometrics. Unidade: ESALQ
Assuntos: ALGORITMOS, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, COBERTURA DO SOLO, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO
ABNT
BARROS, Ana Clara Arantes Villas Bôas de et al. Classification of sugarcane areas in Landsat images using machine learning algorithms. Brazilian Journal of Biometrics, v. 42, p. 147–157, 2024Tradução . . Disponível em: https://doi.org/10.28951/bjb.v42i2.693. Acesso em: 10 set. 2024.APA
Barros, A. C. A. V. B. de, Silva, M. A., Marques, R. D., Lobos, C. M. V., & Luciano, A. C. dos S. (2024). Classification of sugarcane areas in Landsat images using machine learning algorithms. Brazilian Journal of Biometrics, 42, 147–157. doi:10.28951/bjb.v42i2.693NLM
Barros ACAVB de, Silva MA, Marques RD, Lobos CMV, Luciano AC dos S. Classification of sugarcane areas in Landsat images using machine learning algorithms [Internet]. Brazilian Journal of Biometrics. 2024 ; 42 147–157.[citado 2024 set. 10 ] Available from: https://doi.org/10.28951/bjb.v42i2.693Vancouver
Barros ACAVB de, Silva MA, Marques RD, Lobos CMV, Luciano AC dos S. Classification of sugarcane areas in Landsat images using machine learning algorithms [Internet]. Brazilian Journal of Biometrics. 2024 ; 42 147–157.[citado 2024 set. 10 ] Available from: https://doi.org/10.28951/bjb.v42i2.693