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Filtros : "Computers and Electronics in Agriculture" "CANATA, TATIANA FERNANDA" Limpar

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  • Artigo de periodico

    A system for plant detection using sensor fusion approach based on machine learning model (2021)

    Maldaner, Leonardo Felipe ; Molin, José Paulo ; Canata, Tatiana Fernanda ; Martello, Maurício

    Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ

    Assuntos: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, SENSOR

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    • ABNT

      MALDANER, Leonardo Felipe et al. A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture, v. 189, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106382. Acesso em: 15 nov. 2025.

    • APA

      Maldaner, L. F., Molin, J. P., Canata, T. F., & Martello, M. (2021). A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture, 189, 1-11. doi:10.1016/j.compag.2021.106382

    • NLM

      Maldaner LF, Molin JP, Canata TF, Martello M. A system for plant detection using sensor fusion approach based on machine learning model [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106382

    • Vancouver

      Maldaner LF, Molin JP, Canata TF, Martello M. A system for plant detection using sensor fusion approach based on machine learning model [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106382

  • Artigo de periodico

    Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches (2021)

    Maldaner, Leonardo Felipe ; Corrêdo, Lucas de Paula ; Canata, Tatiana Fernanda ; Molin, José Paulo

    Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ

    Assuntos: AGRICULTURA DE PRECISÃO, COMPUTACIONAL, CANA-DE-AÇÚCAR, COLHEDORAS

    Acesso à fonteAcesso à fonteDOIComo citar
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    • ABNT

      MALDANER, Leonardo Felipe et al. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, v. 181, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105945. Acesso em: 15 nov. 2025.

    • APA

      Maldaner, L. F., Corrêdo, L. de P., Canata, T. F., & Molin, J. P. (2021). Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, 181, 1-9. doi:10.1016/j.compag.2020.105945

    • NLM

      Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105945

    • Vancouver

      Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105945
  • 1

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