The Application of Digital Twin Technology in the Development of Intelligent Aquaculture: Status and Opportunities
Jianlei Chen, Yong Xu, Hao Li, Xinguo Zhao, Su Yang, Chengzhen Qi, Keming Qu, Zhengguo Cui
Abstract
Aquaculture is vital for global food security but faces challenges like disease, water quality control, and resource optimization. Digital twin technology, a real-time virtual replica of physical aquaculture systems, emerges as a transformative solution. By integrating sensors and data analytics, it enables monitoring and optimization of water quality, feed efficiency, fish health, and operations. This review explores the current adoption status of digital twins in aquaculture, highlighting applications in real-time monitoring and system optimization. It addresses key implementation challenges, including data integration and scalability, and identifies emerging opportunities for advancing sustainable, intelligent aquaculture practices.