Digital Twin for Agricultural Land Monitoring System

Digital Twin technology has emerged as a promising innovation in agricultural land monitoring by integrating real-time data analysis, simulation, and predictive modelling. However, its application in precision agriculture is still underexplored. Conventional land monitoring methods rely on manual observation or IoT-based systems that often do not provide real-time visualisation and comprehensive data integration. This study developed a Digital Twin-based agricultural land monitoring system that combines sensor and camera data to improve decision-making. The system was applied to rice fields and evaluated for accuracy and efficiency. The results showed a soil condition detection accuracy of 92.5%, an increase in resource efficiency of up to 25%, and an increase in productivity of 22.8%. These findings prove that a Digital Twin technology can optimise agricultural management. This research contributes to the development of intelligent agriculture by providing an interactive monitoring system based on real-time data.