The knowledge generated by animal behavior studies has been gaining importance due to it can be used to improve the efficiency of animal production systems. In recent years, sensor-based approaches for animal behavior classification has emerged as a promising alternative for analyzing animals grazing patterns. In the present article it is proposed the use of a classification system based on inertial sensors for identifying a goat’s grazing behavior in the Argentine Monte Desert. The data acquisition system is based on commercial off-the-self devices. It is used to create a reliable dataset for performing the animal behavior predictions.
By fixing the system on the head of a goat it was possible to log its movements when it was grazing in a natural pasture. A preliminary version of the dataset is evaluated using a classical statistical learning algorithm. Results show that goat activities can be predicted with an average precision value above 85% and a recall of 84%.
Información general
Fecha de exposición:septiembre 2019
Fecha de publicación:2019
Idioma del documento:Inglés
Evento:XI Congreso de AgroInformática (CAI) - JAIIO 48 (Salta, 2019)
Institución de origen:Sociedad Argentina de Informática e Investigación Operativa
Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)