Smart Agriculture Framework with Digital Twin: A Monitoring Model Based on Clustering and prediction of Multivariate Time Series

The current trend in agricultural field management research emphasizes the use of the digital twin due to its importance in digital resource management. Our paper proposes a digital twin architecture for intelligent rice field control by combining IoT sensors, satellite data, and deep learning models to predict yield, weather, and soil conditions to achieve a predefined yield. This approach enables more comprehensive management of agricultural resources, helping farmers make informed decisions to improve yields while reducing costs and environmental impact.