舍饲绵羊运动状态监测方法的研究

Study on the monitoring method of sheep motion state in house feeding

为了解决人工监测舍饲绵羊运动行为耗时费力、效率低下的问题,试验用三轴加速度传感器采集舍饲羊只典型运动行为(采食、行走、反刍行为)数据,对行为数据进行滤波、特征提取和主成分分析后,分别采用支持向量机算法、BP神经网络算法对行为数据进行分类。结果表明:两种方法均能对行为数据进行有效分类,其中支持向量机算法对采食行为的识别率高达97.3%,行为的平均识别率为87.6%;BP神经网络算法对采食、行走和反刍行为的识别率分别为94.0%、93.8%、96.3%,行为的平均识别率为94.6%。说明与支持向量机算法比较,BP神经网络算法可更有效地监测舍饲绵羊的运动状态,并且平均识别率更高。

In order to solve the problems of time-consuming and low efficiency of manual monitoring of house-fed sheep's movement behavior,triaxial acceleration sensors were used to collect typical movement behavior data(feeding,walking and ruminating behaviors)of house-fed sheep.After filtering,feature extraction and principal component analysis,the behavior data were classified by support vector machine(SVM)algorithm and BP neural network algorithm,respectively.The results showed that both methods could effectively classify behavior data,among which the recognition rate of feeding behavior by SVM algorithm was as high as 97.3%,and the average recognition rate of behavior was 87.6%.Based on BP neural network algorithm,the recognition rates of feeding,walking and ruminating behaviors were 94.0%,93.8%and 96.3%,respectively,and the average recognition rate of behaviors was 94.6%.It indicated that the BP neural network algorithm monitor the movement state of sheep in the house more effectively compared with the SVM algorithm,more effective than the support vector machine algorithm in monitoring the movement state of house-fed sheep,and the average recognition rate was higher.