Source: Agronomy Journal. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DOSSEL (BOTÂNICA), FENÓTIPOS, IMAGEAMENTO DE SATÉLITE, MELHORAMENTO GENÉTICO VEGETAL, SOJA
ABNT
MIRANDA, Melissa Cristina de Carvalho et al. High‐throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices. Agronomy Journal, v. 117, p. 1-25, 2025Tradução . . Disponível em: https://doi.org/10.1002/agj2.70012. Acesso em: 30 jan. 2025.APA
Miranda, M. C. de C., Aono, A. H., Fagundes, T. G., Arduini, G. M., & Pinheiro, J. B. (2025). High‐throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices. Agronomy Journal, 117, 1-25. doi:10.1002/agj2.70012NLM
Miranda MC de C, Aono AH, Fagundes TG, Arduini GM, Pinheiro JB. High‐throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices [Internet]. Agronomy Journal. 2025 ; 117 1-25.[citado 2025 jan. 30 ] Available from: https://doi.org/10.1002/agj2.70012Vancouver
Miranda MC de C, Aono AH, Fagundes TG, Arduini GM, Pinheiro JB. High‐throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices [Internet]. Agronomy Journal. 2025 ; 117 1-25.[citado 2025 jan. 30 ] Available from: https://doi.org/10.1002/agj2.70012