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Artigo de periodico
Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] (2024)
Silveira, Robson Mateus Freitas ; Façanha, Débora Andréa Evangelista; McManus, Concepta; Bermejo Asensio, Luis Alberto; Silva, Iran José Oliveira da
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ADAPTAÇÃO ANIMAL, ALGORITMOS, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, PRODUÇÃO ANIMAL, REGULAÇÃO DA TEMPERATURA CORPORAL ANIMAL, SENSOR
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SILVEIRA, Robson Mateus Freitas et al. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502]. Computers and Electronics in Agriculture, v. 218, p. 1, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.108673. Acesso em: 04 nov. 2024.
APA
Silveira, R. M. F., Façanha, D. A. E., McManus, C., Bermejo Asensio, L. A., & Silva, I. J. O. da. (2024). Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502]. Computers and Electronics in Agriculture, 218, 1. doi:10.1016/j.compag.2024.108673
NLM
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108673
Vancouver
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108673
Artigo de periodico
Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning (2024)
Amorim, Magno do Nascimento; Turco, Silvia Helena Nogueira; Costa, Daniel dos Santos; Ferreira, Iara Jeanice Souza; Silva, Wedson Pereira da; Sabino, Antonio Leopoldo Cardoso ; Oliveira da Silva-Miranda, Késia
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ALIMENTAÇÃO ANIMAL, AMOSTRAGEM, APRENDIZADO COMPUTACIONAL, DISPOSITIVOS ELETRÔNICOS, INGESTÃO, INTELIGÊNCIA ARTIFICIAL, OVINOS
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AMORIM, Magno do Nascimento et al. Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning. Computers and Electronics in Agriculture, v. 218, p. 1-8, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.108657. Acesso em: 04 nov. 2024.
APA
Amorim, M. do N., Turco, S. H. N., Costa, D. dos S., Ferreira, I. J. S., Silva, W. P. da, Sabino, A. L. C., & Oliveira da Silva-Miranda, K. (2024). Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning. Computers and Electronics in Agriculture, 218, 1-8. doi:10.1016/j.compag.2024.108657
NLM
Amorim M do N, Turco SHN, Costa D dos S, Ferreira IJS, Silva WP da, Sabino ALC, Oliveira da Silva-Miranda K. Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1-8.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108657
Vancouver
Amorim M do N, Turco SHN, Costa D dos S, Ferreira IJS, Silva WP da, Sabino ALC, Oliveira da Silva-Miranda K. Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1-8.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108657
Artigo de periodico
Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals (2024)
Silveira, Robson Mateus Freitas ; Façanha, Débora Andréa Evangelista; McManus, Concepta; Bermejo Asensio, Luis Alberto; Silva, Iran José Oliveira da
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ADAPTAÇÃO ANIMAL, ALGORITMOS, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, PRODUÇÃO ANIMAL, REGULAÇÃO DA TEMPERATURA CORPORAL ANIMAL, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVEIRA, Robson Mateus Freitas et al. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals. Computers and Electronics in Agriculture, v. 216, p. 1-11, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.108502. Acesso em: 04 nov. 2024.
APA
Silveira, R. M. F., Façanha, D. A. E., McManus, C., Bermejo Asensio, L. A., & Silva, I. J. O. da. (2024). Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals. Computers and Electronics in Agriculture, 216, 1-11. doi:10.1016/j.compag.2023.108502
NLM
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals [Internet]. Computers and Electronics in Agriculture. 2024 ; 216 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.108502
Vancouver
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals [Internet]. Computers and Electronics in Agriculture. 2024 ; 216 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.108502
Artigo de periodico
Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images (2024)
Soares, Victor Hugo Andrade ; Ponti, Moacir Antonelli ; Campello, Ricardo José Gabrielli Barreto
Fonte: Computers and Electronics in Agriculture. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, AERONAVES NÃO TRIPULADAS, PECUÁRIA, AGRICULTURA DE PRECISÃO
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ABNT
SOARES, Victor Hugo Andrade e PONTI, Moacir Antonelli e CAMPELLO, Ricardo José Gabrielli Barreto. Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images. Computers and Electronics in Agriculture, v. 220, p. 1-18, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.108828. Acesso em: 04 nov. 2024.
APA
Soares, V. H. A., Ponti, M. A., & Campello, R. J. G. B. (2024). Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images. Computers and Electronics in Agriculture, 220, 1-18. doi:10.1016/j.compag.2024.108828
NLM
Soares VHA, Ponti MA, Campello RJGB. Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images [Internet]. Computers and Electronics in Agriculture. 2024 ; 220 1-18.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108828
Vancouver
Soares VHA, Ponti MA, Campello RJGB. Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images [Internet]. Computers and Electronics in Agriculture. 2024 ; 220 1-18.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.108828
Artigo de periodico
Development and validation of a mobile application for thermal risk management in pigs – GRT Suínos (2024)
Gregolin, Fabiano ; Miranda, Késia Oliveira da Silva
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ADMINISTRAÇÃO DE RISCO, APLICATIVOS MÓVEIS, CONFORTO TÉRMICO, SUÍNOS
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ABNT
GREGOLIN, Fabiano e MIRANDA, Késia Oliveira da Silva. Development and validation of a mobile application for thermal risk management in pigs – GRT Suínos. Computers and Electronics in Agriculture, v. 222, p. 1-10, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.109054. Acesso em: 04 nov. 2024.
APA
Gregolin, F., & Miranda, K. O. da S. (2024). Development and validation of a mobile application for thermal risk management in pigs – GRT Suínos. Computers and Electronics in Agriculture, 222, 1-10. doi:10.1016/j.compag.2024.109054
NLM
Gregolin F, Miranda KO da S. Development and validation of a mobile application for thermal risk management in pigs – GRT Suínos [Internet]. Computers and Electronics in Agriculture. 2024 ; 222 1-10.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.109054
Vancouver
Gregolin F, Miranda KO da S. Development and validation of a mobile application for thermal risk management in pigs – GRT Suínos [Internet]. Computers and Electronics in Agriculture. 2024 ; 222 1-10.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2024.109054
Artigo de periodico
A Rolling Horizon scheme for rescheduling in agricultural harvest (2023)
Santos, Fernando Montenegro dos; Pérez Galarce, Francisco; Monardes Concha, Carlos; Candia Vejar, Alfredo; Nagano, Marcelo Seido ; Gómez Lagos, Javier
Fonte: Computers and Electronics in Agriculture. Unidade: EESC
Assuntos: COLHEITA, COLHEDORAS, ENGENHARIA DE PRODUÇÃO
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ABNT
SANTOS, Fernando Montenegro dos et al. A Rolling Horizon scheme for rescheduling in agricultural harvest. Computers and Electronics in Agriculture, v. 215, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.108392. Acesso em: 04 nov. 2024.
APA
Santos, F. M. dos, Pérez Galarce, F., Monardes Concha, C., Candia Vejar, A., Nagano, M. S., & Gómez Lagos, J. (2023). A Rolling Horizon scheme for rescheduling in agricultural harvest. Computers and Electronics in Agriculture, 215, 1-15. doi:10.1016/j.compag.2023.108392
NLM
Santos FM dos, Pérez Galarce F, Monardes Concha C, Candia Vejar A, Nagano MS, Gómez Lagos J. A Rolling Horizon scheme for rescheduling in agricultural harvest [Internet]. Computers and Electronics in Agriculture. 2023 ; 215 1-15.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.108392
Vancouver
Santos FM dos, Pérez Galarce F, Monardes Concha C, Candia Vejar A, Nagano MS, Gómez Lagos J. A Rolling Horizon scheme for rescheduling in agricultural harvest [Internet]. Computers and Electronics in Agriculture. 2023 ; 215 1-15.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.108392
Artigo de periodico
Assessing the influence of crop model structure on the performance of data assimilation for sugarcane (2023)
Fattori Junior, Izael Martins ; Marin, Fábio Ricardo
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ALGORITMOS, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, ÍNDICE DE ÁREA FOLIAR, SENSORIAMENTO REMOTO, SIMULAÇÃO
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FATTORI JUNIOR, Izael Martins e MARIN, Fábio Ricardo. Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. Computers and Electronics in Agriculture, v. 209, p. 1-11, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.107848. Acesso em: 04 nov. 2024.
APA
Fattori Junior, I. M., & Marin, F. R. (2023). Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. Computers and Electronics in Agriculture, 209, 1-11. doi:10.1016/j.compag.2023.107848
NLM
Fattori Junior IM, Marin FR. Assessing the influence of crop model structure on the performance of data assimilation for sugarcane [Internet]. Computers and Electronics in Agriculture. 2023 ; 209 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.107848
Vancouver
Fattori Junior IM, Marin FR. Assessing the influence of crop model structure on the performance of data assimilation for sugarcane [Internet]. Computers and Electronics in Agriculture. 2023 ; 209 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.107848
Artigo de periodico
Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits (2023)
Rodrigues, Lucas de Souza; Caixeta Filho, Edmar; Sakiyama, Kenzo ; Santos, Mateus Figueiredo; Jank, Liana ; Carromeu, Camilo ; Silveira, Eloise; Matsubara, Edson Takashi ; Marcato Júnior, José ; Gonçalves, Wesley Nunes
Fonte: Computers and Electronics in Agriculture. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS
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ABNT
RODRIGUES, Lucas de Souza et al. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, v. 211, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.107957. Acesso em: 04 nov. 2024.
APA
Rodrigues, L. de S., Caixeta Filho, E., Sakiyama, K., Santos, M. F., Jank, L., Carromeu, C., et al. (2023). Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, 211, 1-14. doi:10.1016/j.compag.2023.107957
NLM
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.107957
Vancouver
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2023.107957
Artigo de periodico
Digital mapping of coffee ripeness using UAV-based multispectral imagery (2023)
Nogueira Martins, Rodrigo; Pinto, Francisco de Assis de Carvalho; Queiroz, Daniel Marçal de; Valente, Domingos Sárvio Magalhães; Rosas, Jorge Tadeu Fim ; Portes, Marcelo Fagundes; Cerqueira, Elder Sânzio Aguiar
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: AERONAVES NÃO TRIPULADAS, ANÁLISE ESPECTRAL, CAFÉ, MATURAÇÃO VEGETAL, SENSORIAMENTO REMOTO
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ABNT
NOGUEIRA MARTINS, Rodrigo et al. Digital mapping of coffee ripeness using UAV-based multispectral imagery. Computers and Electronics in Agriculture, v. 204, p. 1-13, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2022.107499. Acesso em: 04 nov. 2024.
APA
Nogueira Martins, R., Pinto, F. de A. de C., Queiroz, D. M. de, Valente, D. S. M., Rosas, J. T. F., Portes, M. F., & Cerqueira, E. S. A. (2023). Digital mapping of coffee ripeness using UAV-based multispectral imagery. Computers and Electronics in Agriculture, 204, 1-13. doi:10.1016/j.compag.2022.107499
NLM
Nogueira Martins R, Pinto F de A de C, Queiroz DM de, Valente DSM, Rosas JTF, Portes MF, Cerqueira ESA. Digital mapping of coffee ripeness using UAV-based multispectral imagery [Internet]. Computers and Electronics in Agriculture. 2023 ; 204 1-13.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.107499
Vancouver
Nogueira Martins R, Pinto F de A de C, Queiroz DM de, Valente DSM, Rosas JTF, Portes MF, Cerqueira ESA. Digital mapping of coffee ripeness using UAV-based multispectral imagery [Internet]. Computers and Electronics in Agriculture. 2023 ; 204 1-13.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.107499
Artigo de periodico
Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models (2022)
Turek, Maria Eliza ; Lier, Quirijn de Jong van ; Armindo, Robson André
Fonte: Computers and Electronics in Agriculture. Unidade: CENA
Assuntos: ANÁLISE EM FLUXO CONTÍNUO, ÁGUA DO SOLO, CRESCIMENTO VEGETAL, PRODUÇÃO AGRÍCOLA
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ABNT
TUREK, Maria Eliza e LIER, Quirijn de Jong van e ARMINDO, Robson André. Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models. Computers and Electronics in Agriculture, v. 194, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2022.106801. Acesso em: 04 nov. 2024.
APA
Turek, M. E., Lier, Q. de J. van, & Armindo, R. A. (2022). Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models. Computers and Electronics in Agriculture, 194. doi:10.1016/j.compag.2022.106801
NLM
Turek ME, Lier Q de J van, Armindo RA. Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models [Internet]. Computers and Electronics in Agriculture. 2022 ; 194[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.106801
Vancouver
Turek ME, Lier Q de J van, Armindo RA. Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models [Internet]. Computers and Electronics in Agriculture. 2022 ; 194[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.106801
Artigo de periodico
On supervised learning to model and predict cattle weight in precision livestock breeding (2022)
Biase, Adriele Giaretta ; Albertini, Tiago Zanett ; Mello, Rodrigo Fernandes de
Fonte: Computers and Electronics in Agriculture. Unidade: ICMC
Assuntos: REDES NEURAIS, PROCESSOS ESTOCÁSTICOS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), SISTEMAS DINÂMICOS, PECUÁRIA DE CORTE
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ABNT
BIASE, Adriele Giaretta e ALBERTINI, Tiago Zanett e MELLO, Rodrigo Fernandes de. On supervised learning to model and predict cattle weight in precision livestock breeding. Computers and Electronics in Agriculture, v. 195, p. 1-19, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2022.106706. Acesso em: 04 nov. 2024.
APA
Biase, A. G., Albertini, T. Z., & Mello, R. F. de. (2022). On supervised learning to model and predict cattle weight in precision livestock breeding. Computers and Electronics in Agriculture, 195, 1-19. doi:10.1016/j.compag.2022.106706
NLM
Biase AG, Albertini TZ, Mello RF de. On supervised learning to model and predict cattle weight in precision livestock breeding [Internet]. Computers and Electronics in Agriculture. 2022 ; 195 1-19.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.106706
Vancouver
Biase AG, Albertini TZ, Mello RF de. On supervised learning to model and predict cattle weight in precision livestock breeding [Internet]. Computers and Electronics in Agriculture. 2022 ; 195 1-19.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.106706
Artigo de periodico
Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee (2022)
Gomes, Winston Pinheiro Claro ; Gonçalves, Luís; Silva, Clíssia Barboza da ; Melchert, Wanessa Roberta
Fonte: Computers and Electronics in Agriculture. Unidades: ESALQ, CENA
Assuntos: ANÁLISE DE ALIMENTOS, APRENDIZADO COMPUTACIONAL, CAFÉ, ESPECTROSCOPIA, QUÍMICA DE ALIMENTOS, QUIMIOMETRIA
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ABNT
GOMES, Winston Pinheiro Claro et al. Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee. Computers and Electronics in Agriculture, v. 198, p. 1-9, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2022.107097. Acesso em: 04 nov. 2024.
APA
Gomes, W. P. C., Gonçalves, L., Silva, C. B. da, & Melchert, W. R. (2022). Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee. Computers and Electronics in Agriculture, 198, 1-9. doi:10.1016/j.compag.2022.107097
NLM
Gomes WPC, Gonçalves L, Silva CB da, Melchert WR. Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee [Internet]. Computers and Electronics in Agriculture. 2022 ; 198 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.107097
Vancouver
Gomes WPC, Gonçalves L, Silva CB da, Melchert WR. Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee [Internet]. Computers and Electronics in Agriculture. 2022 ; 198 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2022.107097
Artigo de periodico
Cattle reference growth curves based on centile estimation: a GAMLSS approach (2022)
Nakamura, Luiz R; Ramires, Thiago G; Righetto, Ana J; Pescim, Rodrigo R; Roquim, Fernanda V; Savian, Taciana Villela ; Stasinopoulos, Dimitrios M
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: BOVINOCULTURA DE CORTE, CURVAS DE CRESCIMENTO, GADO HEREFORD, MODELOS MATEMÁTICOS
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ABNT
NAKAMURA, Luiz R et al. Cattle reference growth curves based on centile estimation: a GAMLSS approach. Computers and Electronics in Agriculture, v. 192, p. 1-6, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106572. Acesso em: 04 nov. 2024.
APA
Nakamura, L. R., Ramires, T. G., Righetto, A. J., Pescim, R. R., Roquim, F. V., Savian, T. V., & Stasinopoulos, D. M. (2022). Cattle reference growth curves based on centile estimation: a GAMLSS approach. Computers and Electronics in Agriculture, 192, 1-6. doi:10.1016/j.compag.2021.106572
NLM
Nakamura LR, Ramires TG, Righetto AJ, Pescim RR, Roquim FV, Savian TV, Stasinopoulos DM. Cattle reference growth curves based on centile estimation: a GAMLSS approach [Internet]. Computers and Electronics in Agriculture. 2022 ; 192 1-6.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106572
Vancouver
Nakamura LR, Ramires TG, Righetto AJ, Pescim RR, Roquim FV, Savian TV, Stasinopoulos DM. Cattle reference growth curves based on centile estimation: a GAMLSS approach [Internet]. Computers and Electronics in Agriculture. 2022 ; 192 1-6.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106572
Artigo de periodico
DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage (2021)
Machado, Bruno Brandoli ; Spadon, Gabriel ; Esau, Travis ; Hennessy, Patrick ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Amer-Yahia, Sihem ; Rodrigues Junior, José Fernando
Fonte: Computers and Electronics in Agriculture. Unidade: ICMC
Assuntos: COMPUTAÇÃO MÓVEL, PROCESSAMENTO DE IMAGENS, AGRICULTURA DE PRECISÃO, PESTICIDAS
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ABNT
MACHADO, Bruno Brandoli et al. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage. Computers and Electronics in Agriculture, v. 180, n. Ja 2021, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105906. Acesso em: 04 nov. 2024.
APA
Machado, B. B., Spadon, G., Esau, T., Hennessy, P., Carvalho, A. C. P. de L. F. de, Amer-Yahia, S., & Rodrigues Junior, J. F. (2021). DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage. Computers and Electronics in Agriculture, 180( Ja 2021), 1-9. doi:10.1016/j.compag.2020.105906
NLM
Machado BB, Spadon G, Esau T, Hennessy P, Carvalho ACP de LF de, Amer-Yahia S, Rodrigues Junior JF. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage [Internet]. Computers and Electronics in Agriculture. 2021 ; 180( Ja 2021): 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2020.105906
Vancouver
Machado BB, Spadon G, Esau T, Hennessy P, Carvalho ACP de LF de, Amer-Yahia S, Rodrigues Junior JF. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage [Internet]. Computers and Electronics in Agriculture. 2021 ; 180( Ja 2021): 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2020.105906
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
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: 04 nov. 2024.
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 2024 nov. 04 ] 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 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106382
Artigo de periodico
Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: a detection technique using optical sensors (2021)
Oliveira, Nielsen Moreira ; Medeiros, André Dantas de; Nogueira, Marina de Lima; Arthur, Valter ; Mastrangelo, Thiago de Araújo ; Silva, Clíssia Barboza da
Fonte: Computers and Electronics in Agriculture. Unidades: CENA, ESALQ
Assuntos: MUDAS, RADIAÇÃO GAMA, SEMENTES, SENSORES ÓPTICOS, SOJA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Nielsen Moreira et al. Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: a detection technique using optical sensors. Computers and Electronics in Agriculture, v. 187, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106251. Acesso em: 04 nov. 2024.
APA
Oliveira, N. M., Medeiros, A. D. de, Nogueira, M. de L., Arthur, V., Mastrangelo, T. de A., & Silva, C. B. da. (2021). Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: a detection technique using optical sensors. Computers and Electronics in Agriculture, 187, 1-9. doi:10.1016/j.compag.2021.106251
NLM
Oliveira NM, Medeiros AD de, Nogueira M de L, Arthur V, Mastrangelo T de A, Silva CB da. Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: a detection technique using optical sensors [Internet]. Computers and Electronics in Agriculture. 2021 ; 187 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106251
Vancouver
Oliveira NM, Medeiros AD de, Nogueira M de L, Arthur V, Mastrangelo T de A, Silva CB da. Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: a detection technique using optical sensors [Internet]. Computers and Electronics in Agriculture. 2021 ; 187 1-9.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106251
Artigo de periodico
Cattle counting in the wild with geolocated aerial images in large pasture areas (2021)
Soares, Victor Hugo Andrade ; Ponti, Moacir Antonelli ; Gonçalves, Rodrigo Almeida; Campello, Ricardo José Gabrielli Barreto
Fonte: Computers and Electronics in Agriculture. Unidade: ICMC
Assuntos: AGRICULTURA DE PRECISÃO, PECUÁRIA, RECONHECIMENTO DE IMAGEM, REDES NEURAIS, AERONAVES NÃO TRIPULADAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Victor Hugo Andrade et al. Cattle counting in the wild with geolocated aerial images in large pasture areas. Computers and Electronics in Agriculture, v. 189, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106354. Acesso em: 04 nov. 2024.
APA
Soares, V. H. A., Ponti, M. A., Gonçalves, R. A., & Campello, R. J. G. B. (2021). Cattle counting in the wild with geolocated aerial images in large pasture areas. Computers and Electronics in Agriculture, 189, 1-14. doi:10.1016/j.compag.2021.106354
NLM
Soares VHA, Ponti MA, Gonçalves RA, Campello RJGB. Cattle counting in the wild with geolocated aerial images in large pasture areas [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106354
Vancouver
Soares VHA, Ponti MA, Gonçalves RA, Campello RJGB. Cattle counting in the wild with geolocated aerial images in large pasture areas [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106354
Artigo de periodico
A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance (2021)
Yao, Jie-Peng; Wang, Zi-Yang; Oliveira, Ricardo Ferraz de; Wang, Zhong-Yi; Huang, Lan
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: TRIGO, SALINIDADE DO SOLO, REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS, SOLO SALINO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
YAO, Jie-Peng et al. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance. Computers and Electronics in Agriculture, v. 190, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106435. Acesso em: 04 nov. 2024.
APA
Yao, J. -P., Wang, Z. -Y., Oliveira, R. F. de, Wang, Z. -Y., & Huang, L. (2021). A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance. Computers and Electronics in Agriculture, 190, 1-14. doi:10.1016/j.compag.2021.106435
NLM
Yao J-P, Wang Z-Y, Oliveira RF de, Wang Z-Y, Huang L. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance [Internet]. Computers and Electronics in Agriculture. 2021 ; 190 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106435
Vancouver
Yao J-P, Wang Z-Y, Oliveira RF de, Wang Z-Y, Huang L. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance [Internet]. Computers and Electronics in Agriculture. 2021 ; 190 1-14.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106435
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
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: 04 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. 04 ] 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. 04 ] Available from: https://doi.org/10.1016/j.compag.2020.105945
Artigo de periodico
Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm (2021)
Luciano, Ana Cláudia dos Santos ; Picoli, Michelle Cristina Araújo; Duft, Daniel Garbellini ; Rocha, Jansle Vieira; Leal, Manoel Regis Lima Verde; Maire, Guerric le
Fonte: Computers and Electronics in Agriculture. Unidade: ESALQ
Assuntos: ALGORITMOS, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LUCIANO, Ana Cláudia dos Santos et al. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm. Computers and Electronics in Agriculture, v. 18, p. 1-10, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106063. Acesso em: 04 nov. 2024.
APA
Luciano, A. C. dos S., Picoli, M. C. A., Duft, D. G., Rocha, J. V., Leal, M. R. L. V., & Maire, G. le. (2021). Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm. Computers and Electronics in Agriculture, 18, 1-10. doi:10.1016/j.compag.2021.106063
NLM
Luciano AC dos S, Picoli MCA, Duft DG, Rocha JV, Leal MRLV, Maire G le. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm [Internet]. Computers and Electronics in Agriculture. 2021 ; 18 1-10.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106063
Vancouver
Luciano AC dos S, Picoli MCA, Duft DG, Rocha JV, Leal MRLV, Maire G le. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm [Internet]. Computers and Electronics in Agriculture. 2021 ; 18 1-10.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1016/j.compag.2021.106063

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