Research on a digital twin-based visual crop growth monitoring system

This paper explores the development of a digital twin-based system for visualizing and monitoring crop growth. By integrating real-time sensor data, remote sensing technologies, and advanced computational models, the proposed system provides an accurate, dynamic representation of crop growth stages. It enables farmers and agricultural experts to monitor crop health, predict yields, and make informed decisions regarding irrigation, fertilization, and pest control. The digital twin model facilitates a high level of interactivity and real-time analysis, allowing for continuous adjustments to farming practices based on current conditions. This study also demonstrates the potential of combining digital twin technology with machine learning algorithms to enhance crop monitoring and decision-making processes in precision agriculture.