Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers (2026)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; WEI, MARCELO CHAN FU - ESALQ
- Unidade: ESALQ
- DOI: 10.1016/j.compag.2025.111353
- Subjects: AGRICULTURA DE PRECISÃO; ALGORITMOS; APRENDIZADO COMPUTACIONAL; CANA-DE-AÇÚCAR; INTELIGÊNCIA ARTIFICIAL; MODELOS MATEMÁTICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Computers and Electronics in Agriculture
- ISSN: 0168-1699
- Volume/Número/Paginação/Ano: v. 243, art. 111353, p. 1-16, 2026
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 20 jan. 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.2025.111353 -
NLM
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 jan. 20 ] Available from: https://doi.org/10.1016/j.compag.2025.111353 -
Vancouver
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 jan. 20 ] Available from: https://doi.org/10.1016/j.compag.2025.111353 - High-resolution yield mapping for Eucalyptus grandis: a case study
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Informações sobre o DOI: 10.1016/j.compag.2025.111353 (Fonte: oaDOI API)
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