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Artigo de periodico
Mapping coffee yield with computer vision (2022)
Bazame, Helizani Couto ; Molin, José Paulo ; Althoff, Daniel; Martello, Maurício ; Corrêdo, Lucas de Paula
Source: Precision Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CAFÉ, COLHEDORAS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BAZAME, Helizani Couto et al. Mapping coffee yield with computer vision. Precision Agriculture, p. 1-16, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11119-022-09924-0. Acesso em: 15 nov. 2024.
APA
Bazame, H. C., Molin, J. P., Althoff, D., Martello, M., & Corrêdo, L. de P. (2022). Mapping coffee yield with computer vision. Precision Agriculture, 1-16. doi:10.1007/s11119-022-09924-0
NLM
Bazame HC, Molin JP, Althoff D, Martello M, Corrêdo L de P. Mapping coffee yield with computer vision [Internet]. Precision Agriculture. 2022 ; 1-16.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/s11119-022-09924-0
Vancouver
Bazame HC, Molin JP, Althoff D, Martello M, Corrêdo L de P. Mapping coffee yield with computer vision [Internet]. Precision Agriculture. 2022 ; 1-16.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/s11119-022-09924-0
Artigo de periodico
Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy (2021)
Corrêdo, Lucas de Paula ; Maldaner, Leonardo Felipe ; Bazame, Helizani Couto ; Molin, José Paulo
Source: Sensors. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, AMOSTRAGEM, CANA-DE-AÇÚCAR, ESPECTROSCOPIA INFRAVERMELHA, QUIMIOMETRIA, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CORRÊDO, Lucas de Paula et al. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors, v. 21, p. 1-23, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21062195. Acesso em: 15 nov. 2024.
APA
Corrêdo, L. de P., Maldaner, L. F., Bazame, H. C., & Molin, J. P. (2021). Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors, 21, 1-23. doi:10.3390/s21062195
NLM
Corrêdo L de P, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy [Internet]. Sensors. 2021 ; 21 1-23.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/s21062195
Vancouver
Corrêdo L de P, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy [Internet]. Sensors. 2021 ; 21 1-23.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/s21062195
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
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, COMPUTACIONAL, CANA-DE-AÇÚCAR, COLHEDORAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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. 2024.
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 2024 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 2024 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105945

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