Herrmann, Paulo Sergio de Paula 
; Luccas, Matheus dos Santos ; Ferreira, Ednaldo José ; Torre Neto, Andre
Fonte: Frontiers in Plant Science. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, FENÓTIPOS, ABSORÇÃO DE ÁGUA PELAS PLANTAS, RESPIRAÇÃO VEGETAL, SOJA
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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: 20 maio 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 maio 20 ] 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 maio 20 ] Available from: https://doi.org/10.3389/fpls.2024.1323296
