Source: Computers and Electronics in Agriculture. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS
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RODRIGUES, Lucas de Souza et al. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, v. 211, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.107957. Acesso em: 23 ago. 2024.APA
Rodrigues, L. de S., Caixeta Filho, E., Sakiyama, K., Santos, M. F., Jank, L., Carromeu, C., et al. (2023). Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, 211, 1-14. doi:10.1016/j.compag.2023.107957NLM
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2024 ago. 23 ] Available from: https://doi.org/10.1016/j.compag.2023.107957Vancouver
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2024 ago. 23 ] Available from: https://doi.org/10.1016/j.compag.2023.107957