Maldaner, Leonardo Felipe 
; Corrêdo, Lucas de Paula ; Canata, Tatiana Fernanda ; Molin, José Paulo
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: AGRICULTURA DE PRECISÃO, COMPUTACIONAL, CANA-DE-AÇÚCAR, COLHEDORAS
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MALDANER, Leonardo Felipe et al. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, v. 181, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105945. Acesso em: 15 nov. 2025.APA
Maldaner, L. F., Corrêdo, L. de P., Canata, T. F., & Molin, J. P. (2021). Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, 181, 1-9. doi:10.1016/j.compag.2020.105945NLM
Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105945Vancouver
Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105945
