IoT-enabled recurrent spatio-temporal adaptive attention of temporal convolutional transformer with continual learning for dairy farming

Abstract

Cattle health and behavior monitoring is critical in the maintenance of livestock welfare and efficient farm productivity. However, meaningful features for a health alert system are hard to extract from voluminous data generated by IoT sensors, which track various health and environmental parameters. In this paper, a new system along these lines for challenges as described above is presented: an advanced alert system using the Recurrent Spatio-Temporal Adaptive Attention of Temporal Convolutional Transformer (RecSTAA-TCT) model. The newly proposed model has integrated the following major components: a dynamic residual bidirectional gated recurrent unit, intensive spatial attention, and a Temporal Adaptive Temporal Convolutional Transformer module. This makes feature extraction with time series data rather challenging due to the complexity and variability of the data generated by IoT sensors. The Adaptive Residual Bi-GRU achieves this by efficiently handling the temporal dependencies to improve the robustness of the model in the presence of missing data and noise. Critical spatial features are then extracted by intensive spatial attention, hence allowing the system to focus attention only on the most informative data in a biometric and environmental database. The Temporal Adaptive TCT module refines further the model capability by extracting temporal features to make precise predictions that trigger the alert system in response to possible health and behavioral anomalies. It embeds continual learning into the model, through which it learns new patterns and data with time, giving back predictive accuracy and reliability to the model. It is based on this foundation that an integrated approach provides proactive management and timely interventions, hence substantially improving real-time anomaly detection over the traditional methods of monitoring. The proposed RecSTAA-TCT model is giving a classification accuracy of 96.5% and delivering alert notifications at a response time of 10.3 s.