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Artigo de periodico
Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices (2025)
Alexandre, Marta Laura de Souza ; Lima, Izabelle de Lima e; Nilsson, Matheus Sterzo; Rizzo, Rodnei ; Silva, Carlos Augusto Alves Cardoso ; Fiorio, Peterson Ricardo
Source: Agronomy. Unidade: ESALQ
Subjects: ANÁLISE ESPECTRAL, APRENDIZADO COMPUTACIONAL, BIOMETRIA, CANA-DE-AÇÚCAR, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALEXANDRE, Marta Laura de Souza et al. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices. Agronomy, v. 15, p. 1-20, 2025Tradução . . Disponível em: https://doi.org/10.3390/agronomy15092149. Acesso em: 27 nov. 2025.
APA
Alexandre, M. L. de S., Lima, I. de L. e, Nilsson, M. S., Rizzo, R., Silva, C. A. A. C., & Fiorio, P. R. (2025). Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices. Agronomy, 15, 1-20. doi:10.3390/agronomy15092149
NLM
Alexandre ML de S, Lima I de L e, Nilsson MS, Rizzo R, Silva CAAC, Fiorio PR. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices [Internet]. Agronomy. 2025 ; 15 1-20.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy15092149
Vancouver
Alexandre ML de S, Lima I de L e, Nilsson MS, Rizzo R, Silva CAAC, Fiorio PR. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices [Internet]. Agronomy. 2025 ; 15 1-20.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy15092149
Artigo de periodico
Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy (2024)
Tavares, Marcos Silva ; Silva, Carlos Augusto Alves Cardoso ; Regazzo, Jamile Raquel ; Sardinha, Edson José de Souza ; Silva, Thiago Lima da ; Fiorio, Peterson Ricardo ; Baesso, Murilo Mesquita
Source: Agronomy. Unidades: FZEA, ESALQ
Subjects: ALGORITMOS, AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, FEIJÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAVARES, Marcos Silva et al. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, v. 14, p. 1-14, 2024Tradução . . Disponível em: https://doi.org/10.3390/agronomy14081634. Acesso em: 27 nov. 2025.
APA
Tavares, M. S., Silva, C. A. A. C., Regazzo, J. R., Sardinha, E. J. de S., Silva, T. L. da, Fiorio, P. R., & Baesso, M. M. (2024). Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, 14, 1-14. doi:10.3390/agronomy14081634
NLM
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy14081634
Vancouver
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy14081634
Artigo de periodico
Evaluation of the effect of the vigor of soybean seeds treated with micronutrientsusing X-ray fluorescence spectroscopy and hyperspectral imaging (2023)
Alves, Rafael Mateus ; Gomes-Junior, Francisco Guilhien ; Carmo-Filho, Abimael dos Santos ; Ribeiro, Glória de Freitas Rocha; Rego, Carlos Henrique Queiroz ; Iost-Filho, Fernando Henrique ; Yamamoto, Pedro Takao
Source: Agronomy. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA DE RAIO X, GERMINAÇÃO DE SEMENTES, MICRONUTRIENTES, PROCESSAMENTO DE IMAGENS, SEMENTES, SOJA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALVES, Rafael Mateus et al. Evaluation of the effect of the vigor of soybean seeds treated with micronutrientsusing X-ray fluorescence spectroscopy and hyperspectral imaging. Agronomy, v. 13, p. 1-19, 2023Tradução . . Disponível em: https://doi.org/10.3390/agronomy13071945. Acesso em: 27 nov. 2025.
APA
Alves, R. M., Gomes-Junior, F. G., Carmo-Filho, A. dos S., Ribeiro, G. de F. R., Rego, C. H. Q., Iost-Filho, F. H., & Yamamoto, P. T. (2023). Evaluation of the effect of the vigor of soybean seeds treated with micronutrientsusing X-ray fluorescence spectroscopy and hyperspectral imaging. Agronomy, 13, 1-19. doi:10.3390/agronomy13071945
NLM
Alves RM, Gomes-Junior FG, Carmo-Filho A dos S, Ribeiro G de FR, Rego CHQ, Iost-Filho FH, Yamamoto PT. Evaluation of the effect of the vigor of soybean seeds treated with micronutrientsusing X-ray fluorescence spectroscopy and hyperspectral imaging [Internet]. Agronomy. 2023 ; 13 1-19.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy13071945
Vancouver
Alves RM, Gomes-Junior FG, Carmo-Filho A dos S, Ribeiro G de FR, Rego CHQ, Iost-Filho FH, Yamamoto PT. Evaluation of the effect of the vigor of soybean seeds treated with micronutrientsusing X-ray fluorescence spectroscopy and hyperspectral imaging [Internet]. Agronomy. 2023 ; 13 1-19.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/agronomy13071945

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