CICYTTP   12500
CENTRO DE INVESTIGACION CIENTIFICA Y DE TRANSFERENCIA TECNOLOGICA A LA PRODUCCION
Unidad Ejecutora - UE
artículos
Título:
Estimación del consumo en rumiantes en pastoreo utilizando redes neuronales artificiales
Autor/es:
JULIO R GALLI; MARIELA NOELIA UHRIG; DIEGO HUMBERTO MILONE; HUGO LEONARDO RUFINER
Revista:
Revista Eletrônica Argentina-Brasil de Tecnologias da Informação e da Comunicação: v.1.
Editorial:
v. 1 n. 8 (2017): Revista Eletrônica Argentina-Brasil de Tecnologias da Informação e Comunicação
Referencias:
Lugar: Três de Maio; Año: 2018 vol. 1
ISSN:
2446-7634
Resumen:
Accurate and rapid measurement of forage intake in ruminants is important for efficient management livestock and forage resources, as well as for animal health and welfare in production systems. The use of intelligent signal processing algorithms to extract relevant information from the sound emitted by ruminants is a promising method to predict the intake of ruminants in grazing conditions. In this work, multilayer perceptrons and extreme learning machines, are used as non-linear multivariate regression models to predict intake. The results show that these non-linear regression techniques can significantly reduce the error in the estimation of forage intake in ruminants.