Design a Deep Learning Model for Dairy Cow Abnormal Behaviour Prediction

Abstract

When it comes to determining the health and well-being of cattle, it is critical to observe their behaviour. Another strong and cost-effective monitoring approach is a neural network-based monitoring system that analyses time series data from inertial sensors attached to cows. This is one of the most powerful and cost-effective monitoring systems currently available. Even though deep learning has made significant advances in pattern detection, large datasets are still required in many cases. When only a little amount of data is available, data augmentation is a very effective and low-cost pre-processing step for neural network-based systems. To study and analysis, a variety of ways for enhancing inertial sensor data based on cow behavioural data are discussed. Using convolutional neural networks, researchers are attempting to solve the difficult problem of identifying cow behaviour, which is made much more difficult by a scarcity of training data. Our proposed approaches areimproving the effectiveness of deep learning (CNN+LSTM) in the classification of cow behaviour while also lowering the total system costs associated with data collection and labelling.