Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Tian, Fuyang^1 ; Cao, Dong^1 ; Dong, Xiaoning^1 ; Zhao, Xinqiang^1 ; Li, Fade^1 ; Wang, Zhonghua^2

College of Mechanical and Electronic Engineering, Shandong Agriculture University, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipments, Tai'an, China^1

College of Animal Science and Technology, Shangdong Agriculture University, Tai'an, China^2

关键词: Activity index;    Behavioral features;    Detection methods;    Heat detection;    k-Means algorithm;    Lower-power consumption;    Technical progress;    Wireless communication system;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012069/pdf
DOI  :  10.1088/1755-1315/69/1/012069

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.