Source: Frontiers in Plant Science. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, FENÓTIPOS, ABSORÇÃO DE ÁGUA PELAS PLANTAS, RESPIRAÇÃO VEGETAL, SOJA
ABNT
HERRMANN, Paulo Sergio de Paula et al. Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime. Frontiers in Plant Science, v. 15, p. 1-15, 2024Tradução . . Disponível em: https://doi.org/10.3389/fpls.2024.1323296. Acesso em: 30 ago. 2024.APA
Herrmann, P. S. de P., Luccas, M. dos S., Ferreira, E. J., & Torre Neto, A. (2024). Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime. Frontiers in Plant Science, 15, 1-15. doi:10.3389/fpls.2024.1323296NLM
Herrmann PS de P, Luccas M dos S, Ferreira EJ, Torre Neto A. Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime [Internet]. Frontiers in Plant Science. 2024 ; 15 1-15.[citado 2024 ago. 30 ] Available from: https://doi.org/10.3389/fpls.2024.1323296Vancouver
Herrmann PS de P, Luccas M dos S, Ferreira EJ, Torre Neto A. Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime [Internet]. Frontiers in Plant Science. 2024 ; 15 1-15.[citado 2024 ago. 30 ] Available from: https://doi.org/10.3389/fpls.2024.1323296