Digital Twin technologies are virtual representations of physical farming systems that integrate real-time data, simulation models, and predictive analytics to support monitoring and agricultural decision-making. They are increasingly recognised as a cornerstone of digital transformation in agriculture and have demonstrated strong potential for improving precision farming, resource optimisation, and input efficiency.
Abstract
Digital Twin technologies are virtual representations of physical farming systems that integrate real-time data, simulation models, and predictive analytics to support monitoring and agricultural decision-making. They are increasingly recognised as a cornerstone of digital transformation in agriculture and have demonstrated strong potential for improving precision farming, resource optimisation, and input efficiency. However, their application remains fragmented, with limited integration into broader climate adaptation, climate risk, and ecological monitoring frameworks. Similarly, although smart agriculture and precision farming technologies have advanced productivity and resource-use efficiency, their adoption remains uneven across regions and production scales.
This systematic review explores the convergence of emerging technologies and sustainability imperatives in agriculture by synthesising insights from 118 peer-reviewed journal articles published between 2014 and 2024. The review focuses on five intersecting thematic domains: