ReP USP - Resultado da busca

Artigo de periodico
Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms (2023)
Falcioni, Renan ; Gonçalves, João Vitor Ferreira ; Oliveira, Karym Mayara de ; Oliveira, Caio Almeida de ; Demattê, José Alexandre Melo ; Antunes, Werner Camargos ; Nanni, Marcos Rafael
Source: Plants. Unidade: ESALQ
Subjects: ALFACE, FENÓTIPOS, PIGMENTOS VEGETAIS, ESPECTROSCOPIA, INTELIGÊNCIA ARTIFICIAL, VARIEDADES VEGETAIS, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
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: 11 nov. 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/plants12061333
NLM
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 nov. 11 ] Available from: https://doi.org/10.3390/plants12061333
Vancouver
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 nov. 11 ] Available from: https://doi.org/10.3390/plants12061333
Artigo de periodico
Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data (2023)
Mallah, Sina ; Delsouz Khaki, Bahareh; Davatgar, Naser; Poppiel, Raul Roberto ; Demattê, José Alexandre Melo
Source: AgriEngineering. Unidade: ESALQ
Subjects: GRANULOMETRIA DO SOLO, INTELIGÊNCIA ARTIFICIAL, MAPEAMENTO DO SOLO, REDES NEURAIS, RELEVO, TEXTURA DO SOLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALLAH, Sina et al. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, v. 5, p. 40–64, 2023Tradução . . Disponível em: https://doi.org/10.3390/agriengineering5010004. Acesso em: 11 nov. 2024.
APA
Mallah, S., Delsouz Khaki, B., Davatgar, N., Poppiel, R. R., & Demattê, J. A. M. (2023). Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data. AgriEngineering, 5, 40–64. doi:10.3390/agriengineering5010004
NLM
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 nov. 11 ] Available from: https://doi.org/10.3390/agriengineering5010004
Vancouver
Mallah S, Delsouz Khaki B, Davatgar N, Poppiel RR, Demattê JAM. Digital mapping of topsoil texture classes using a hybridized classical statistics–artificial neural networks approach and relief data [Internet]. AgriEngineering. 2023 ; 5 40–64.[citado 2024 nov. 11 ] Available from: https://doi.org/10.3390/agriengineering5010004
Artigo de periodico
Artificially intelligent soil quality and health indices for ‘next generation’ food production systems (2021)
Andrade, Vinícius Henrique Gomes Zuppa de; Redmile-Gordon, Marc; Barbosa, Bruno Henrique Groenner; Andreote, Fernando Dini ; Roesch, Luiz Fernando Wurdig; Pylro, Victor Satler
Source: Trends in Food Science & Technology. Unidade: ESALQ
Subjects: INTELIGÊNCIA ARTIFICIAL, MICROBIOLOGIA DO SOLO, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANDRADE, Vinícius Henrique Gomes Zuppa de et al. Artificially intelligent soil quality and health indices for ‘next generation’ food production systems. Trends in Food Science & Technology, v. 107, p. 195–200, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.tifs.2020.10.018. Acesso em: 11 nov. 2024.
APA
Andrade, V. H. G. Z. de, Redmile-Gordon, M., Barbosa, B. H. G., Andreote, F. D., Roesch, L. F. W., & Pylro, V. S. (2021). Artificially intelligent soil quality and health indices for ‘next generation’ food production systems. Trends in Food Science & Technology, 107, 195–200. doi:10.1016/j.tifs.2020.10.018
NLM
Andrade VHGZ de, Redmile-Gordon M, Barbosa BHG, Andreote FD, Roesch LFW, Pylro VS. Artificially intelligent soil quality and health indices for ‘next generation’ food production systems [Internet]. Trends in Food Science & Technology. 2021 ; 107 195–200.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1016/j.tifs.2020.10.018
Vancouver
Andrade VHGZ de, Redmile-Gordon M, Barbosa BHG, Andreote FD, Roesch LFW, Pylro VS. Artificially intelligent soil quality and health indices for ‘next generation’ food production systems [Internet]. Trends in Food Science & Technology. 2021 ; 107 195–200.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1016/j.tifs.2020.10.018
Parte de monografia/livro
Soil erodibility and its influential factors in the Middle East (2021)
Ostovari, Y; Moosavi, A. A; Mozaffari, H; Poppiel, Raul Roberto ; Tayebi, Mahboobeh ; Dematte, Jose Alexandre Melo
Source: Computers in earth and environmental sciences : artificial intelligence and advanced technologies in hazards and risk management. Unidade: ESALQ
Subjects: ADMINISTRAÇÃO DE RISCO AMBIENTAL, APRENDIZADO COMPUTACIONAL, CONSERVAÇÃO DO SOLO, EROSÃO, INTELIGÊNCIA ARTIFICIAL, MODELAGEM DE DADOS, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OSTOVARI, Y et al. Soil erodibility and its influential factors in the Middle East. Computers in earth and environmental sciences : artificial intelligence and advanced technologies in hazards and risk management. Tradução . Amsterdam: Elsevier, 2021. . Disponível em: https://doi.org/10.1016/B978-0-323-89861-4.00037-3. Acesso em: 11 nov. 2024.
APA
Ostovari, Y., Moosavi, A. A., Mozaffari, H., Poppiel, R. R., Tayebi, M., & Dematte, J. A. M. (2021). Soil erodibility and its influential factors in the Middle East. In Computers in earth and environmental sciences : artificial intelligence and advanced technologies in hazards and risk management. Amsterdam: Elsevier. doi:10.1016/B978-0-323-89861-4.00037-3
NLM
Ostovari Y, Moosavi AA, Mozaffari H, Poppiel RR, Tayebi M, Dematte JAM. Soil erodibility and its influential factors in the Middle East [Internet]. In: Computers in earth and environmental sciences : artificial intelligence and advanced technologies in hazards and risk management. Amsterdam: Elsevier; 2021. [citado 2024 nov. 11 ] Available from: https://doi.org/10.1016/B978-0-323-89861-4.00037-3
Vancouver
Ostovari Y, Moosavi AA, Mozaffari H, Poppiel RR, Tayebi M, Dematte JAM. Soil erodibility and its influential factors in the Middle East [Internet]. In: Computers in earth and environmental sciences : artificial intelligence and advanced technologies in hazards and risk management. Amsterdam: Elsevier; 2021. [citado 2024 nov. 11 ] Available from: https://doi.org/10.1016/B978-0-323-89861-4.00037-3

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