A review of traditional and machine learning methods applied to animal breeding.
Anim Health Res Rev
; 20(1): 31-46, 2019 06.
Article
en En
| MEDLINE
| ID: mdl-31895018
ABSTRACT
The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Cruzamiento
/
Ganado
/
Aprendizaje Automático
/
Crianza de Animales Domésticos
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
Límite:
Animals
Idioma:
En
Revista:
Anim Health Res Rev
Asunto de la revista:
MEDICINA VETERINARIA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Canadá