Machine learning to support geographical origin traceability of Coffea arabica (2022)
Source: Advances in Artificial Intelligence and Machine Learning. Research. Unidades: CENA, ESALQ, ECOLOGIA APLICADA
Subjects: APRENDIZADO COMPUTACIONAL, CAFÉ, DENOMINAÇÃO DE ORIGEM
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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: 17 out. 2024.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 2024 out. 17 ] 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 2024 out. 17 ] Available from: https://doi.org/10.54364/AAIML.2022.1118