Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, ÁRVORES FLORESTAIS, FLORESTAS, INVENTÁRIO FLORESTAL, REDES NEURAIS, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
ABNT
CORTE, Ana Paula Dalla et al. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, v. 179, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105815. Acesso em: 21 out. 2024.APA
Corte, A. P. D., Souza, D. V., Rex, F. E., Sanquetta, C. R., Mohan, M., Silva, C. A., et al. (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 1-14. doi:10.1016/j.compag.2020.105815NLM
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2024 out. 21 ] Available from: https://doi.org/10.1016/j.compag.2020.105815Vancouver
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2024 out. 21 ] Available from: https://doi.org/10.1016/j.compag.2020.105815