Source: Plants. Unidade: ESALQ
Subjects: ALFACE, FENÓTIPOS, PIGMENTOS VEGETAIS, ESPECTROSCOPIA, INTELIGÊNCIA ARTIFICIAL, VARIEDADES VEGETAIS, ALGORITMOS
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FALCIONI, Renan et al. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms. Plants, v. 12, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.3390/plants12061333. Acesso em: 03 jul. 2024.APA
Falcioni, R., Gonçalves, J. V. F., Oliveira, K. M. de, Oliveira, C. A. de, Demattê, J. A. M., Antunes, W. C., & Nanni, M. R. (2023). Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms. Plants, 12, 1-15. doi:10.3390/plants12061333NLM
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Demattê JAM, Antunes WC, Nanni MR. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms [Internet]. Plants. 2023 ; 12 1-15.[citado 2024 jul. 03 ] Available from: https://doi.org/10.3390/plants12061333Vancouver
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Demattê JAM, Antunes WC, Nanni MR. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms [Internet]. Plants. 2023 ; 12 1-15.[citado 2024 jul. 03 ] Available from: https://doi.org/10.3390/plants12061333