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Artigo de periodico
Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance (2024)
Prado, Melina ; Famoso, Adam; Guidry, Kurt; Fritsche-Neto, Roberto
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, ARROZ, CLIMA, INTERAÇÃO GENÓTIPO-AMBIENTE, MODELOS MATEMÁTICOS, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PRADO, Melina et al. Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance. Frontiers in Plant Science, v. 15, p. 1-13, 2024Tradução . . Disponível em: https://doi.org/10.3389/fpls.2024.1458701. Acesso em: 27 nov. 2025.
APA
Prado, M., Famoso, A., Guidry, K., & Fritsche-Neto, R. (2024). Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance. Frontiers in Plant Science, 15, 1-13. doi:10.3389/fpls.2024.1458701
NLM
Prado M, Famoso A, Guidry K, Fritsche-Neto R. Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance [Internet]. Frontiers in Plant Science. 2024 ; 15 1-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2024.1458701
Vancouver
Prado M, Famoso A, Guidry K, Fritsche-Neto R. Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance [Internet]. Frontiers in Plant Science. 2024 ; 15 1-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2024.1458701
Artigo de periodico
Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids (2022)
Galli, Giovanni ; Sabadin, F; Yassue, Rafael Massahiro ; Galves, C; Carvalho, H. F; Crossa, J; Montesinos-López, O. A; Fritsche-Neto, Roberto
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, GENÔMICA, HIBRIDAÇÃO VEGETAL, MILHO, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GALLI, Giovanni et al. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, v. 13, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.3389/fpls.2022.845524. Acesso em: 27 nov. 2025.
APA
Galli, G., Sabadin, F., Yassue, R. M., Galves, C., Carvalho, H. F., Crossa, J., et al. (2022). Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, 13, 1-13. doi:10.3389/fpls.2022.845524
NLM
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2022.845524
Vancouver
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2022.845524
Artigo de periodico
An approach using emerging optical technologies and artificial intelligence brings new markers to evaluate peanut seed quality (2022)
Oliveira, Gustavo Roberto Fonseca de; Mastrangelo, Clíssia Barboza ; Hirai, Welinton Yoshio ; Batista, Thiago Barbosa; Sudki, Julia Marconato ; Petronilio, Ana Carolina Picinini; Crusciol, Carlos Alexandre Costa; Silva, Edvaldo Aparecido Amaral da
Source: Frontiers in Plant Science. Unidades: CENA, ESALQ
Subjects: AMENDOIM, ANÁLISE ESPECTRAL, APRENDIZADO COMPUTACIONAL, FLUORESCÊNCIA, INTELIGÊNCIA ARTIFICIAL, SEMENTES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Gustavo Roberto Fonseca de et al. An approach using emerging optical technologies and artificial intelligence brings new markers to evaluate peanut seed quality. Frontiers in Plant Science, v. 13, p. 1-18, 2022Tradução . . Disponível em: https://doi.org/10.3389/fpls.2022.849986. Acesso em: 27 nov. 2025.
APA
Oliveira, G. R. F. de, Mastrangelo, C. B., Hirai, W. Y., Batista, T. B., Sudki, J. M., Petronilio, A. C. P., et al. (2022). An approach using emerging optical technologies and artificial intelligence brings new markers to evaluate peanut seed quality. Frontiers in Plant Science, 13, 1-18. doi:10.3389/fpls.2022.849986
NLM
Oliveira GRF de, Mastrangelo CB, Hirai WY, Batista TB, Sudki JM, Petronilio ACP, Crusciol CAC, Silva EAA da. An approach using emerging optical technologies and artificial intelligence brings new markers to evaluate peanut seed quality [Internet]. Frontiers in Plant Science. 2022 ; 13 1-18.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2022.849986
Vancouver
Oliveira GRF de, Mastrangelo CB, Hirai WY, Batista TB, Sudki JM, Petronilio ACP, Crusciol CAC, Silva EAA da. An approach using emerging optical technologies and artificial intelligence brings new markers to evaluate peanut seed quality [Internet]. Frontiers in Plant Science. 2022 ; 13 1-18.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2022.849986
Artigo de periodico
Integrating optical imaging tools for rapid and non-invasive characterization of seed quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as study cases (2020)
Galletti, Patrícia Aparecida; Carvalho, Marcia Eugenia Amaral ; Hirai, Welinton Yoshio ; Brancaglioni, Vivian Aparecida ; Arthur, Valter ; Silva, Clíssia Barboza da
Source: Frontiers in Plant Science. Unidades: CENA, ESALQ
Subjects: ANÁLISE ESPECTRAL, APRENDIZADO COMPUTACIONAL, CENOURA, FOTOSSÍNTESE, QUIMIOMETRIA, SEMENTES, TOMATE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GALLETTI, Patrícia Aparecida et al. Integrating optical imaging tools for rapid and non-invasive characterization of seed quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as study cases. Frontiers in Plant Science, v. 11, p. 1-17, 2020Tradução . . Disponível em: https://doi.org/10.3389/fpls.2020.577851. Acesso em: 27 nov. 2025.
APA
Galletti, P. A., Carvalho, M. E. A., Hirai, W. Y., Brancaglioni, V. A., Arthur, V., & Silva, C. B. da. (2020). Integrating optical imaging tools for rapid and non-invasive characterization of seed quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as study cases. Frontiers in Plant Science, 11, 1-17. doi:10.3389/fpls.2020.577851
NLM
Galletti PA, Carvalho MEA, Hirai WY, Brancaglioni VA, Arthur V, Silva CB da. Integrating optical imaging tools for rapid and non-invasive characterization of seed quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as study cases [Internet]. Frontiers in Plant Science. 2020 ; 11 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2020.577851
Vancouver
Galletti PA, Carvalho MEA, Hirai WY, Brancaglioni VA, Arthur V, Silva CB da. Integrating optical imaging tools for rapid and non-invasive characterization of seed quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as study cases [Internet]. Frontiers in Plant Science. 2020 ; 11 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3389/fpls.2020.577851

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