Vision computacional y aprendizaje profundo para el fenotipado de alto rendimiento en plantas de interés agronómico

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03_VisionComputacional_y_AprendizajeProfundo.pdf (6.84 MB)

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Date Issued

2024-06-12

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Citation

Arrechea, D. (2024) Vision computacional y aprendizaje profundo para el fenotipado de alto rendimiento en plantas de interés agronómico​. Presented to Universidad Industrial de Santander – MAPI3 on 12 de Junio de 2024. 29 sl.

Abstract/Description

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https://hdl.handle.net/10568/145376

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Author ORCID identifiers

Darwin Alexis Arrechea-Castillo  https://orcid.org/0000-0002-2395-2181

Contributes to SDGs

SDG 1 - No povertySDG 2 - Zero hungerSDG 8 - Decent work and economic growthSDG 13 - Climate action

Organizations Affiliated to the Authors

International Center for Tropical Agriculture

Investors/sponsors

CGIAR Trust Fund

CGIAR Action Areas

Genetic Innovation

CGIAR Impact Areas

Nutrition, health and food security

CGIAR Initiatives

Accelerated Breeding

Collections

Alliance Bioversity CIAT Posters, Presentations, and Infographics
Alliance Research Lever 6: Crops for Nutrition and Health
CGIAR Initiative on Accelerated Breeding

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Type

Presentation

Review Status

Language

es

AGROVOC Keywords

phenotypes; urochloa; algorithms; hybrid seed production; producción de semillas híbridas; algoritmos; fenotipado de alto rendimiento

Subjects

LIVESTOCK; PLANT BREEDING; PLANT GENETIC RESOURCES; TROPICAL FORAGES

Countries

Colombia

Regions

Americas; South America; Latin America and the Caribbean

Access Rights

Open Access Open Access

Usage Rights

CC-BY-4.0

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