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: DENOMINAÇÃO DE ORIGEM, QUALIDADE DOS ALIMENTOS, CAFÉ
ABNT
FERNANDES, Elisabete A. De Nadai; SARRIES, Gabriel Adrian; MAZOLA, Yuniel Tejeda; et al. Machine learning to support geographical origin traceability of Coffea arabica. Advances in Artificial Intelligence and Machine Learning. Research, New Delhi, v. 2, n. 1, p. 273-287, 2022. DOI: 10.54364/AAIML.2022.1118.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. Advances in Artificial Intelligence and Machine Learning. Research. 2022 ; 2( 1): 273-287.Vancouver
Fernandes EADN, Sarries GA, Mazola YT, Lima RC, Furlan GN, Bacchi MA. Machine learning to support geographical origin traceability of Coffea arabica. Advances in Artificial Intelligence and Machine Learning. Research. 2022 ; 2( 1): 273-287.