Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization--Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel.
Sensors (Basel). 2011;11(1):362-83. doi: 10.3390/s110100362. Epub 2010 Dec 31.
The advent of the Global Positioning System (GPS) has transformed our ability to track livestock on rangelands. However, GPS data use would be greatly enhanced if we could also infer the activity timeline of an animal. We tested how well animal activity could be inferred from data provided by Lotek GPS collars, alone or in conjunction with IceRobotics IceTag pedometers. The collars provide motion and head position data, as well as location. The pedometers count steps, measure activity levels, and differentiate between standing and lying positions. We gathered synchronized data at 5-min resolution, from GPS collars, pedometers, and human observers, for free-grazing cattle (n = 9) at the Hatal Research Station in northern Israel. Equations for inferring activity during 5-min intervals (n = 1,475), classified as Graze, Rest (or Lie and Stand separately), and Travel were derived by discriminant and partition (classification tree) analysis of data from each device separately and from both together. When activity was classified as Graze, Rest and Travel, the lowest overall misclassification rate (10%) was obtained when data from both devices together were subjected to partition analysis; separate misclassification rates were 8, 12, and 3% for Graze, Rest and Travel, respectively. When Rest was subdivided into Lie and Stand, the lowest overall misclassification rate (10%) was again obtained when data from both devices together were subjected to partition analysis; misclassification rates were 6, 1, 26, and 17% for Graze, Lie, Stand, and Travel, respectively. The primary problem was confusion between Rest (or Stand) and Graze. Overall, the combination of Lotek GPS collars with IceRobotics IceTag pedometers was found superior to either device alone in inferring animal activity.
全球定位系统（GPS）的出现改变了我们在牧场追踪牲畜的能力。然而，如果我们还能推断出动物的活动时间，那么 GPS 数据的使用将会得到极大的增强。我们测试了仅使用或结合使用 Lotek GPS 项圈和 IceRobotics IceTag 计步器来推断动物活动的效果。项圈提供运动和头部位置数据以及位置信息。计步器可以记录步数、测量活动水平，并区分站立和躺下的姿势。我们在以色列北部的 Hatal 研究站以自由放牧的奶牛（n=9）为对象，以 5 分钟的分辨率收集了来自 GPS 项圈、计步器和人工观测者的同步数据。我们分别对每个设备的数据以及两个设备的数据进行判别和分区（分类树）分析，推导出了推断 5 分钟间隔内活动（n=1,475）的方程，将活动分为 Grazing、Rest（或 Lie 和 Stand 分别）和 Travel。当将活动分类为 Grazing、Rest 和 Travel 时，当将两个设备的数据一起进行分区分析时，总体错误分类率最低（10%）；分别为 Grazing、Rest 和 Travel 的错误分类率为 8%、12%和 3%。当将 Rest 细分为 Lie 和 Stand 时，当将两个设备的数据一起进行分区分析时，总体错误分类率再次最低（10%）；错误分类率分别为 Grazing、Lie、Stand 和 Travel 的 6%、1%、26%和 17%。主要问题是将 Rest（或 Stand）与 Grazing 混淆。总体而言，Lotek GPS 项圈与 IceRobotics IceTag 计步器的结合比单独使用任何一种设备都更能准确推断动物的活动。
