Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming.

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.