AI Based Digital Twin Model for Cattle Caring
This work addresses cattle health monitoring for farmers, but it is incremental as it applies existing deep learning methods to a new agricultural dataset.
The paper developed an AI-powered digital twin model for cattle health monitoring using sensor data from a farm IoT system, enabling real-time tracking and prediction of physiological cycles, with findings that cattle treated with topical anaesthetic and meloxicam showed the least pain reaction.
In this paper, we developed innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work was built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle health based on Deep Learning (DL) was generated using the sensor data acquired from the farm IoT system. The health and physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data which is required to validate the legitimacy of the digital twins model. In terms of behavioural state, it was found that the cattle treated with a combination of topical anaesthetic and meloxicam exhibits the least pain reaction. The digital twins model developed in this work can be used to monitor the health of cattle