AI Based Digital Twin Model for Cattle Caring

TL;DR

This paper presents an AI-powered digital twin model for cattle health monitoring, utilizing IoT sensor data and deep learning to track and predict physiological states in real time.

Contribution

It introduces a novel digital twin framework for cattle health that leverages IoT and deep learning, enabling real-time monitoring and prediction of physiological cycles.

Findings

Cattle treated with topical anaesthetic and meloxicam show minimal pain reactions.

The digital twin accurately models cattle health and physiological cycles.

Real-time monitoring enhances cattle health management.

Abstract

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