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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
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
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. 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
Artigo de periodico
Detection, classification, and mapping of coffee fruits during harvest with computer vision (2021)
Bazame, Helizani Couto ; Molin, José Paulo ; Althoff, Daniel; Martello, Maurício
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CAFÉ, COLHEITA, MATURAÇÃO VEGETAL, REDES NEURAIS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BAZAME, Helizani Couto et al. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture, v. 183, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106066. Acesso em: 15 nov. 2025.
APA
Bazame, H. C., Molin, J. P., Althoff, D., & Martello, M. (2021). Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture, 183, 1-11. doi:10.1016/j.compag.2021.106066
NLM
Bazame HC, Molin JP, Althoff D, Martello M. Detection, classification, and mapping of coffee fruits during harvest with computer vision [Internet]. Computers and Electronics in Agriculture. 2021 ; 183 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106066
Vancouver
Bazame HC, Molin JP, Althoff D, Martello M. Detection, classification, and mapping of coffee fruits during harvest with computer vision [Internet]. Computers and Electronics in Agriculture. 2021 ; 183 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106066
Artigo de periodico
Yield mapping methods for manually harvested crops (2020)
Colaço, A.F; Trevisan, R.G; Karp, F.H.S; Molin, Jose Paulo
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: COLHEITA, FRUTAS, HORTALIÇAS, MAPAS, PRODUTIVIDADE, SAFRA, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLAÇO, A.F et al. Yield mapping methods for manually harvested crops. Computers and Electronics in Agriculture, v. 177, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105693. Acesso em: 15 nov. 2025.
APA
Colaço, A. F., Trevisan, R. G., Karp, F. H. S., & Molin, J. P. (2020). Yield mapping methods for manually harvested crops. Computers and Electronics in Agriculture, 177, 1-14. doi:10.1016/j.compag.2020.105693
NLM
Colaço AF, Trevisan RG, Karp FHS, Molin JP. Yield mapping methods for manually harvested crops [Internet]. Computers and Electronics in Agriculture. 2020 ; 177 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105693
Vancouver
Colaço AF, Trevisan RG, Karp FHS, Molin JP. Yield mapping methods for manually harvested crops [Internet]. Computers and Electronics in Agriculture. 2020 ; 177 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105693
Artigo de periodico
Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion (2016)
Spekken, Mark; Bruin, Sytze de; Molin, José Paulo; Sparovek, Gerd
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: SISTEMA DE INFORMAÇÃO GEOGRÁFICA, ESCOAMENTO, EROSÃO, CULTIVO EM FAIXA, MÁQUINAS AGRÍCOLAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPEKKEN, Mark et al. Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion. Computers and Electronics in Agriculture, v. 124, p. 194–210, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2016.03.013. Acesso em: 15 nov. 2025.
APA
Spekken, M., Bruin, S. de, Molin, J. P., & Sparovek, G. (2016). Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion. Computers and Electronics in Agriculture, 124, 194–210. doi:10.1016/j.compag.2016.03.013
NLM
Spekken M, Bruin S de, Molin JP, Sparovek G. Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion [Internet]. Computers and Electronics in Agriculture. 2016 ; 124 194–210.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2016.03.013
Vancouver
Spekken M, Bruin S de, Molin JP, Sparovek G. Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion [Internet]. Computers and Electronics in Agriculture. 2016 ; 124 194–210.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2016.03.013

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