Machine learning to support geographical origin traceability of Coffea arabica (2022)
Source: Advances in Artificial Intelligence and Machine Learning. Research. Unidades: CENA, ESALQ, Interunidades em Ecologia Aplicada
Subjects: APRENDIZADO COMPUTACIONAL, CAFÉ, DENOMINAÇÃO DE ORIGEM
ABNT
FERNANDES, Elisabete A. De Nadai et al. Machine learning to support geographical origin traceability of Coffea arabica. Advances in Artificial Intelligence and Machine Learning. Research, v. 2, n. 1, p. 273-287, 2022Tradução . . Disponível em: https://doi.org/10.54364/AAIML.2022.1118. Acesso em: 26 abr. 2025.APA
Fernandes, E. A. D. N., Sarries, G. A., Mazola, Y. T., Lima, R. C., Furlan, G. N., & Bacchi, M. A. (2022). Machine learning to support geographical origin traceability of Coffea arabica. Advances in Artificial Intelligence and Machine Learning. Research, 2( 1), 273-287. doi:10.54364/AAIML.2022.1118NLM
Fernandes EADN, Sarries GA, Mazola YT, Lima RC, Furlan GN, Bacchi MA. Machine learning to support geographical origin traceability of Coffea arabica [Internet]. Advances in Artificial Intelligence and Machine Learning. Research. 2022 ; 2( 1): 273-287.[citado 2025 abr. 26 ] Available from: https://doi.org/10.54364/AAIML.2022.1118Vancouver
Fernandes EADN, Sarries GA, Mazola YT, Lima RC, Furlan GN, Bacchi MA. Machine learning to support geographical origin traceability of Coffea arabica [Internet]. Advances in Artificial Intelligence and Machine Learning. Research. 2022 ; 2( 1): 273-287.[citado 2025 abr. 26 ] Available from: https://doi.org/10.54364/AAIML.2022.1118