Freedom to lie: How farrowing environment affects sow lying behaviour assessment using inertial sensors
Freedom to lie: How farrowing environment affects sow lying behaviour assessment using inertial sensors
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DOI：
10.1016/j.compag.2019.01.035
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发表时间：
2019-02-01
影响因子：
8.3
通讯作者：
Kyriazakis, Ilias
中科院分区：
文献类型：
--
作者：
Thompson, Robin J.;Matthews, Stephen;Kyriazakis, Ilias
我们解决了使用加速度计来自动监测自由分娩母猪的躺卧行为;由于它们的运动自由度以及随之而来的母猪能够表现出的运动种类的增加，自动化这一挑战比在运动限制分娩环境中饲养的母猪更大。开发的方法被应用到两个突出的应用：通过检测筑巢活动的分娩预测，和比较产妇躺在传统的运动限制和自由分娩系统的行为。将两个传感器连接在8头围产期母猪的前端和后端。记录分娩前后5天的运动行为。通过支持向量机分类器对活动转换进行分类，分别使用来自两个传感器的数据，并结合;分类器输出针对从视频数据收集的地面实况注释进行验证。我们得出结论的好处，使用多个传感器在一个单一的传感器，以及适合不同的传感器位置的母猪。发现通过使用多个传感器，活动分类得到改善，与单独使用前传感器（平均F-1 = 0.49）和单独使用后传感器（平均F-1 = 0.57）相比，平均F-1评分（0和1之间的预测性能指标）为0.84。使用双传感器设置对活动转换进行分类，平均F-1评分为0.77。使用基于阈值的方法，将转换频率作为筑巢行为的指标，我们能够检测到筑巢的开始，平均分娩延迟为11.1（+/- 4.65）小时，平均每头母猪检测到1次过早;然而，大多数过早发生在特定母猪身上。我们画自由分娩和限制母猪之间的说谎行为的比较。使用混合设计方差分析，我们发现分娩环境对过渡时间（p = 0.003），峰值加速度（p = 0.007）和音调变化率（p = 0.009）的主要影响。通过添加多个传感器来提高母猪活动转变的分类准确性，可以提高诸如分娩预测等应用的性能，该应用能够通过实现分娩监督来降低仔猪死亡率。了解运动限制如何影响分娩母猪的躺卧行为有可能为关于限制母猪和自由分娩环境的发展的决定提供信息。
We address the use of accelerometery to automatically monitor lying behaviour in free-farrowing sows; due to their freedom of movement and the consequent increased variety of movements the sows are able to exhibit, the challenges in automating this are greater than in sows housed in movement restricting farrowing environments. The methodology developed was applied to two salient applications: that of farrowing prediction through detection of nest building activity, and comparison of maternal lying behaviour in conventional movement-restricting and free-farrowing systems. Two sensors were attached at both the front and hind end to each of eight periparturient sows. Movement behaviour was recorded for a period of five days around parturition. Activity transitions were classified by a Support Vector Machine classifier, using data from both sensors individually, and combined; classifier output was validated against ground truth annotations collected from video data. We draw conclusions about the benefits of using multiple sensors over a single sensor, as well as the suitability of different sensor locations on the sow. Activity classification was found to improve through the use of multiple sensors, with a mean F-1 score (a measure of predictive performance between 0 and 1) of 0.84, compared to use of the front sensor alone (mean F-1 = 0.49) and the hind sensor alone (mean F-1 = 0.57). Activity transitions were classified using the dual sensor setup with a mean F-1 score of 0.77. Using a threshold-based approach, taking transition frequency as an indicator of nesting behaviour, we were able to detect the onset of nest building with an average latency to farrowing of 11.1 (+/- 4.65) hours, and an average of 1 premature detection per sow; however, the majority of these premature were in a particular sow. We draw comparisons between the lying behaviour of free-farrowing and restricted sows. Using a mixed-design ANOVA we found a main effect of farrowing environment on transition duration (p = 0.003), peak acceleration (p = 0.007) and rate of change in pitch (p = 0.009). Improving the classification accuracy of sow activity transitions through the addition of multiple sensors allows for improved performance in applications such as farrowing prediction, which has the capacity to reduce piglet mortality through enabling farrowing supervision. Understanding how movement restriction affects the lying behaviour of farrowing sows has the potential to inform decisions regarding restriction of sows and development of free-farrowing environments.