Digital Twin Technology for Sustainable Animal Housing Environment Management: Opportunities and Challenges

Abstract

Digital Twin Technology is emerging as a transformative solution for sustainable animal housing environment management by creating synchronized virtual representations of physical livestock facilities. However, existing studies remain fragmented, emphasizing isolated sensing or automation technologies while providing limited discussion of integrated Digital Twin frameworks, sustainability outcomes, and implementation barriers. This review presents a comprehensive analysis of Digital Twin technology for animal housing, focusing on its architecture. The study examines physical, virtual, and communication layers, IoT-enabled sensing, edge-cloud computing, artificial intelligence, and machine learning for continuous monitoring, prediction, and decision support. It further evaluates environmental parameters, including temperature, humidity, ventilation, air quality, lighting, and animal behavioral indicators influencing welfare and productivity. The review highlights opportunities in precision livestock farming, climate-resilient production, predictive infrastructure maintenance, autonomous environmental control, and resource optimization. Additionally, critical challenges related to sensor reliability, calibration, deployment cost, interoperability, cybersecurity, data privacy, and scalability are discussed. The review identifies research gaps and outlines future directions involving explainable artificial intelligence, federated learning, digital sustainability metrics, and standardized Digital Twin platforms.