Mallah, Sina 
; Delsouz Khaki, Bahareh; Davatgar, Naser; Poppiel, Raul Roberto ; Demattê, José Alexandre Melo
Source: AgriEngineering. Unidade: ESALQ
Subjects: GRANULOMETRIA DO SOLO, INTELIGÊNCIA ARTIFICIAL, MAPEAMENTO DO SOLO, REDES NEURAIS, RELEVO, TEXTURA DO SOLO
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MALLAH, Sina et al. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, v. 5, p. 40–64, 2023Tradução . . Disponível em: https://doi.org/10.3390/agriengineering5010004. Acesso em: 02 nov. 2024.APA
Mallah, S., Delsouz Khaki, B., Davatgar, N., Poppiel, R. R., & Demattê, J. A. M. (2023). Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, 5, 40–64. doi:10.3390/agriengineering5010004NLM
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 nov. 02 ] Available from: https://doi.org/10.3390/agriengineering5010004Vancouver
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 nov. 02 ] Available from: https://doi.org/10.3390/agriengineering5010004
