Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.
Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.
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DOI：
10.3390/s18103532
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发表时间：
2018-10-19
期刊：
影响因子：
--
通讯作者：
Kaler J
中科院分区：
文献类型：
--
作者：
Mansbridge N;Mitsch J;Bollard N;Ellis K;Miguel-Pacheco GG;Dottorini T;Kaler J
放牧和反刍是反刍动物最重要的行为，因为它们每天的大部分时间都在进行这些活动。持续监测反刍动物的进食行为是监测反刍动物健康、生产力和福利的重要手段。然而，由人类操作员进行的监测容易受到人为差异的影响，耗时且成本高，特别是对牧场或自由放养的动物。使用传感器自动获取数据，使用软件对行为进行分类和识别，为解决这些问题提供了巨大的潜力。在这项工作中，通过连接到耳朵和衣领的加速度计/陀螺仪传感器从绵羊收集的数据，以16 Hz采样，用于使用各种机器学习算法开发放牧和反刍行为的分类器：随机森林（RF），支持向量机（SVM），k最近邻（kNN）和自适应提升（Adaboost）。从信号中提取的多个特征根据其分类的重要性进行排名。几个性能指标被认为是比较分类器作为一个功能的算法，传感器定位和使用的功能。随机森林产生了最高的整体准确性：92%的衣领和91%的耳朵。基于陀螺仪的特征被证明对饮食行为具有最大的相对重要性。从耳朵和衣领的数据，被纳入模型的功能特性的最佳数量为39。研究结果表明，人们可以成功地以非常高的准确度对绵羊的进食行为进行分类;这可以用于开发一种自动监测绵羊饲料摄入量的设备，以监测健康和福利。
Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.
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影响因子：
2.3
作者：
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