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Identifying livestock behavior patterns based on accelerometer dataset
Journal of Computational Science ( IF 3.3 ) Pub Date : 2020-02-14 , DOI: 10.1016/j.jocs.2020.101076
Domingo S. Rodriguez-Baena , Francisco A. Gomez-Vela , Miguel García-Torres , Federico Divina , Carlos D. Barranco , Norberto Daz-Diaz , Manuel Jimenez , Gema Montalvo

In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed.

In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming.



中文翻译:

基于加速度计数据集识别牲畜行为模式

在大型畜牧业中,能够自动检测动物的行为将是有益的。实际上,这将可以估计个人的健康状况,从而为饲养者提供有价值的见解。传统上,此过程仅依靠饲养员的经验手动进行。这种方法对少数个人有效。但是,在大型繁殖场中,这可能并不是最好的方法,因为这样一来,并非所有动物都能一直得到有效监控。而且,传统方法严重依赖于人类经验,而人类经验不能总是被认为是理所当然的。为此,本文提出了一种自动检测动物活动和非活动时间段的新方法,作为牲畜的行为指标。为此,

特别是,使用伊比利亚猪和犊牛收集的数据测试了该方法的可靠性。结果证实了该方法可以帮助育种者发现大型畜牧业的活动期和非活动期。

更新日期:2020-02-14
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