NB-IoT Estrus Detection System of Dairy Cows Based on LSTM Networks

Nan Ma, Lin Pan, Shihao Chen, Baoling Liu

Abstract: To improve the revenue of dairy farms, cow estrus must be accurately monitored to track mating time. Narrow Band Internet of Things (NB-IoT) is considered as a promising technology to realize cost-effective detection system attributing to its wide coverage and low power consumption. To increase the success rate of real-time detection, machine learning based algorithms have been applied to extract patterns from estrus data. However, due to the lack of multivariate time series data, most previous studies do not consider using the time correlation to guide estrus detection. In this paper, we present a NB-IoT based solution framework where multivariate behavioral time series data collected by neck-mounted sensors and then uploaded to a cloud data center for further analysis through NB-IoT network. Based on the collected data, we propose an estrus prediction algorithm which gives estrus alert by exploiting Long-Short Term Memory (LSTM) and Convolution Neural Network (CNN). Through numerical studies conducted using real data set from the pasture, we show that our proposed solution outperforms exiting detection algorithms in terms of accuracy and efficiency.