基于神经网络的奶牛发情行为辨识与预测研究

Oestrus Detection and Prediction in Dairy Cows Based on Neural Networks

及时和准确地识别奶牛发情在牛群管理中具有重要地位。根据奶牛发情期活动量上升、静卧时间变短和体温升高等生理学特性,采用振动传感器、姿态传感器和温度传感器实时检测奶牛的活动量、静卧时间和体温等参数。建立了以奶牛行走步数、静卧时间、行走时间、温度为输入,以奶牛行为特征为输出的LVQ神经网络发情行为辨识模型与预测模型。经过初步试验验证,设计的监测系统和神经网络辨识算法检测奶牛发情的准确率可达100%,发情预测率达到70%以上。

It is important to detect cow oestrus in time and accurately.According to the characteristics of increasing activity,shorter repose time and increasing animal heat during cow oestrus,vibrational sensor and temperature sensor were used to detect the above parameters.LVQ neural network model was built,which taking walking steps,repose time and animal heat as input,and behavior characteristics as output.The results showed that,with the proposed algorithm,the correct detection rate was up to 100%,and prediction rate for oestrus was more than 70%.