Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ALGORITMOS, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, INTELIGÊNCIA ARTIFICIAL, MODELOS MATEMÁTICOS
ABNT
WEI, Marcelo Chan Fu e MOLIN, José Paulo e LONGCHAMPS, Louis. Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers. Computers and Electronics in Agriculture, v. 243, p. 1-16, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2025.111353. Acesso em: 12 fev. 2026.APA
Wei, M. C. F., Molin, J. P., & Longchamps, L. (2026). Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers. Computers and Electronics in Agriculture, 243, 1-16. doi:10.1016/j.compag.2025.111353NLM
Wei MCF, Molin JP, Longchamps L. Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers [Internet]. Computers and Electronics in Agriculture. 2026 ; 243 1-16.[citado 2026 fev. 12 ] Available from: https://doi.org/10.1016/j.compag.2025.111353Vancouver
Wei MCF, Molin JP, Longchamps L. Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers [Internet]. Computers and Electronics in Agriculture. 2026 ; 243 1-16.[citado 2026 fev. 12 ] Available from: https://doi.org/10.1016/j.compag.2025.111353
