Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units

•LSTM-RNN model was used to monitor and classify cattle behavior patterns using inertial measurement unit.•Eight behavior patterns including social behavior were classified.•Classify accuracy of periodic and regular behaviors were higher than irregular and fluctuant behaviors.•The LSTM-RNN models achieved their best performance when using window-sizes 64. In this study, we aimed to develop a recurrent neural network (RNN) with a long short-time memory (LSTM) model to monitor and classify cattle behavior patterns using inertial measurement units (IMU). This model was trained using motion data obtained from 6 Japanese steers. Each steer was fitted with an IMU sensor inside a waterproof box attached to a collar on the neck. Classified behavior classes included feeding, lying, ruminating (while lying), ruminating (while standing), licking salt, moving, social licking and head butt. LSTM-RNN model was trained to classify and measure cattle’s behavior across three window-sizes including window-size 64, 128 and 256 (3.2 s, 6.4 s and 12.8 s). A convolution neural network (CNN) model was used for comparison. The results reveal the LSTM-RNN model classification performance was superior to the CNN model. The LSTM-RNN model was found to achieve the best performance when using a window-size of 64 (accuracy, precision, recall, f1-score all were 88.7%). With a window-size 64, classification accuracy of specific behaviors was 97.8% (feeding), 88.7% (lying), 88.4% (ruminating-lying), 92.9% (ruminating-standing), 94.4% (licking salt), 84.8% (moving), 80.3% (social licking), and 81.9% (head butt). A few physically similar behaviors were easily misclassified. In conclusion, the LSTM-RNN demonstrated reasonable classification of cattle behavior. In future, additional sensors, such as a microphone, could be added to the cattle behavior monitoring system and behavior classification extended to cattle welfare and growth behaviors, such as feeding, reproduction and disease prediction.