摘要
奶牛等大型动物的疾病和发情状况目前主要依赖饲养员目测判断,大规模集约化养殖仍采用人工观测方法,这不仅带来繁重的人力负担,也容易误判。为了能自动准确地识别奶牛是否发情或生病,该文提出在奶牛颈部安装无线传感器节点,通过各种传感器获取奶牛的体温、呼吸频率和运动加速度等参数,采用K-均值聚类算法对提取的各种参数进行行为特征多级分类识别,以此建立的动物行为监测系统能准确区分奶牛静止、慢走、爬跨等行为特征,从而可以长时间监测奶牛的健康状态。而且,这种监测系统易于推广到对其他动物的监测,对促进养殖业和畜牧业的发展也具有指导意义。
Nowadays estimation of disease and estrus conditions for large animals such as cattle mainly relies on farmer＇s visual judgment. This practice by manual observation had an obvious fundamental restriction,which a large number of experienced farmers were required. Therefore,it was not cost-effective to be adopted in large scale and intensive breeding. This approach also inclined to have unreliable detection rate due to human errors. In order to recognize estrus or diseases of cattle automatically and accurately,this paper presented a monitoring system based on wireless sensor node installed on a cattle＇s neck. Body temperature,respiratory rate,movement acceleration and other parameters of the cattle could be easily captured through a variety of sensors on the node. The collected data were sorted out and classified into different classes according to the behavioral features extracted by using the K-means clustering algorithm. This process enabled an empirical detection model to be developed which could then be applied routinely to predict estrus or diseases. Experimental results showed that the monitoring models could effectively distinguish behaviors such as standing,walking,mounting etc.,and estrus or diseases could be observed accurately based on these unique features. This methodology in behavioral modeling could be adapted in monitoring other animals and holds great importance for development of other stockbreeding.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2010年第3期203-208,I0005,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家高技术研究发展计划资助项目(2006AA10Z246)
