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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

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García-Fortea, E.; García-Pérez, A.; Gimeno -Páez, E.; Sánchez-Gimeno, A.; Vilanova Navarro, S.; Prohens Tomás, J.; Pastor-Calle, D. (2020). A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant. Biology. 9(9):1-19. https://doi.org/10.3390/biology9090272

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/167458

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Título: A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant
Autor: García-Fortea, Edgar García-Pérez, Ana Gimeno -Páez, Esther Sánchez-Gimeno, Alfredo Vilanova Navarro, Santiago Prohens Tomás, Jaime Pastor-Calle, David
Entidad UPV: Universitat Politècnica de València. Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana - Institut Universitari de Conservació i Millora de l'Agrodiversitat Valenciana
Universitat Politècnica de València. Departamento de Biotecnología - Departament de Biotecnologia
Fecha difusión:
Resumen:
[EN] The development of double haploids (DHs) is a straightforward path for obtaining pure lines but has multiple bottlenecks. Among them is the determination of the optimal stage of pollen induction for androgenesis. In ...[+]
Palabras clave: Androgenesis , Anther culture , Microspores , RetinaNet , Solanum melongena
Derechos de uso: Reconocimiento (by)
Fuente:
Biology. (eissn: 2079-7737 )
DOI: 10.3390/biology9090272
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/biology9090272
Código del Proyecto:
info:eu-repo/grantAgreement/AEI//RTI2018-094592-B-100/
info:eu-repo/grantAgreement/MECD//FPU17%2F02389/
Agradecimientos:
This research was funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion and Fondo Europeo de Desarrollo Regional (grant RTI-2018-094592-B-I00 from MCIU/AEI/FEDER, UE). ...[+]
Tipo: Artículo

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