摘要
为了解决人工监测草原放牧牛运动行为工作量大、监测精度低的问题,试验提出一种基于二叉决策树分类模型的草原牛行为识别方法,即选取草原牛颈部三轴加速度计采集数据的X轴、Y轴、Z轴方差、均方根、平均值及三轴总体的矢量幅度(signal vector magnitude, SVM)和幅度(singal magnitude area, SMA)共11种统计特征量来构建查准率-查全率曲线(P-R曲线),通过P-R曲线获取各统计特征量所对应的最优行为类别分组方式及最优阈值,利用信息增益作为选择标准来构建二叉决策树分类模型,运用此模型对草原牛的躺卧、反刍、采食及慢走四种运动行为进行分类识别,并与K-均值(K-means)聚类算法比对。结果表明:K-means聚类算法只能识别躺卧行为,难以区分反刍、采食及慢走三种运动行为,但二叉决策树分类模型能够有效地将躺卧和慢走行为从躺卧、反刍、采食及慢走四种行为中识别出来,查准率和查全率均达到0.760以上。说明二叉决策树分类模型较常用的K-means聚类算法可更有效地完成草原牛行为分类,并且准确率更高。
In order to solve the problem of heavy workload and low accuracy in the manual monitoring of grassland grazing cattle, this paper proposed a grassland cattle movement behavior recognition method based on a binary decision-tree classification model. The P-R curve was constructed by selecting the X-axis, Y-axis, Z-axis variance, root mean square, average, and three-axis overall signal vector magnitude(SVM) and signal magnitude area(SMA) of the three-axis acceleration sensor data in the grassland cattle neck. The grouping method of the optimal behavior category and the optimal threshold corresponding to each statistical feature quantity were obtained by P-R curve.The information gain was used as the selection criterion to construct a binary decision-tree classification model, and this model was used to classify and recognize the four types of movement behaviors of prairie cattle: lying, ruminating, feeding and walking, which were compared with the K-means clustering algorithm.The results showed that the K-means clustering algorithm could only recognize lying behaviors, but it was difficult to distinguish three other kinds of behaviors: ruminating, feeding and walking.However, the binary decision-tree classification model could effectively identify lying and walking behaviors from ruminating and feeding behaviors. The accuracy rates and the precision rates reached 0.760 or more. The results indicated that the binary decision-tree classification model could complete the grassland cattle behavior classification more effectively than the commonly used K-means algorithm, and the accuracy rate was higher.
作者
刘章华
魏凤歧
李琦
张燕
LIU Zhanghua;WEI Fengqi;LI Qi;ZHANG Yan(College of Information Engineering,Pioneer College,Inner Mongolia University,Hohhot 010070,China;College of Information Engineering,Inner Mongolia University of Science&Technology,Baotou 014010,China)
出处
《黑龙江畜牧兽医》
CAS
北大核心
2022年第4期53-58,136,共7页
Heilongjiang Animal Science And veterinary Medicine
基金
内蒙古自治区科技重大专项(2019ZD025)
内蒙古自治区科技成果转化项目“基于物联网的现代畜牧业生产监管及产品溯源服务平台推广应用”(CGZH2018041)
内蒙古大学创业学院教师科研基金项目。
关键词
加速度传感器
K-MEANS聚类算法
二叉决策树
行为分类
无线传感器网络
草原牛
数据处理
acceleration sensor
K-means clustering algorithm
binary decision-tree
behavior classification
wireless sensor network
grassland cattle
data processing
