基于IMU的细粒度奶牛行为判别

针对奶牛行为判别自动化水平不足、准确率低的问题,采用惯性测量单元(IMU)和卷积神经网络(CNN),对细粒度奶牛行为判别进行研究.结果表明:1)在KNN、SVM、BPNN、CNN和LSTM 5个模型中,CNN模型在奶牛行为分类测试集上的准确率最高.2)含有三轴加速度计、陀螺仪和磁力计的IMU更加适用于奶牛行为分类,其分...

Fine-grained cows' behavior classification method based on IMU

In order to realize the automatic recognition of dairy cows'' behaviors, an fine-grained cow behavior classification method based on inertial measurement unit(IMU)and convolutional neural network(CNN)was proposed. The results showed that: 1)Among the five classification models of KNN, SVM, BPNN, CNN and LSTM, the CNN model has the highest accuracy on the cow behavior classification test set. 2)The IMU with triaxial accelerometer, gyroscope and magnetometer is more suitable for cow behavior classification, and its classification effect of cow behavior is better than IMU with a single type of sensor. 3)Sampling frequency was related to the performance of classification models. The higher the frequency, the higher the accuracy. When the sampling frequency was set to 25 Hz, the performance of classification model was the best. 4)Among the three time windows of 1, 2 and 4 s, the performance of behavior classification model of cows with the time window of 4 s is the best. 5)When the optimal configuration was adopted, the CNN model can effectively distinguish the standing and lying states of dairy cows with a correct rate of 99. 08%; The six behaviors of cows'' feeding, chewing, standing ruminating, lying ruminating, lying resting, standing resting can be identified, and the accuracy is 85. 19%. In conclusion, the proposed method can be used to distinguish the fine-grained behaviors of dairy cows effectively and support the automatic and intelligent management of dairy farming.