A novel image-based approach for soybean seed phenotyping using machine learning techniques (2022)
- Authors:
- Autor USP: MIRANDA, MELISSA CRISTINA DE CARVALHO - ESALQ
- Unidade: ESALQ
- DOI: 10.1101/2022.10.10.511645
- Subjects: APRENDIZADO COMPUTACIONAL; FENÓTIPOS; MELHORAMENTO GENÉTICO VEGETAL; PROCESSAMENTO DE IMAGENS; REDES NEURAIS; SEMENTES; SOJA
- Language: Inglês
- Imprenta:
- Publisher place: Cold Spring Harbor, NY
- Date published: 2022
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MIRANDA, Melissa Cristina de Carvalho e AONO, Alexandre Hild e PINHEIRO, José Baldin. A novel image-based approach for soybean seed phenotyping using machine learning techniques. bioRxiv, p. 1-32 (preprint), 2022Tradução . . Disponível em: https://doi.org/10.1101/2022.10.10.511645. Acesso em: 14 fev. 2026. -
APA
Miranda, M. C. de C., Aono, A. H., & Pinheiro, J. B. (2022). A novel image-based approach for soybean seed phenotyping using machine learning techniques. bioRxiv, 1-32 (preprint). doi:10.1101/2022.10.10.511645 -
NLM
Miranda MC de C, Aono AH, Pinheiro JB. A novel image-based approach for soybean seed phenotyping using machine learning techniques [Internet]. bioRxiv. 2022 ; 1-32 (preprint).[citado 2026 fev. 14 ] Available from: https://doi.org/10.1101/2022.10.10.511645 -
Vancouver
Miranda MC de C, Aono AH, Pinheiro JB. A novel image-based approach for soybean seed phenotyping using machine learning techniques [Internet]. bioRxiv. 2022 ; 1-32 (preprint).[citado 2026 fev. 14 ] Available from: https://doi.org/10.1101/2022.10.10.511645 - From seed to canopy: high-throughput phenotyping and machine learning in soybean breeding
- Productivity and quality of cotton fiber in different planting seasons
- High‐throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices
Informações sobre o DOI: 10.1101/2022.10.10.511645 (Fonte: oaDOI API)
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