DEVELOPING A DIGITALTWIN MODEL FOR CORN, WHEAT AND RAPESEED YIELDS COMPUTATION
2024
Catalin CHITU | Ilinca IMBREA | Stefan BATRINA | Florin IMBREA
Digital Twin is an emerging agritech technology that involves creating virtual representations of physical systems, which can be used for various purposes, such as optimizing crop management, predicting yield, and managing resources efficiently. The research is focusing to build a accurate digital twin model for crop growth, considering factors like evaporation (ET), growing degrees days (GDD), crop type, soil data, and agricultural practices. The model handles data streams related with geolocation, IOT historical sensor data and weather forecasts streams to simulate the crop risk and yield. Frequent updates based on real-time data enhance accuracy. Aside essential water management crop flow, the model is processing historical data related with nutrients like nitrogen (N), phosphorus (P), and potassium (K) elements are vital for plant growth and health, and their optimal balance can significantly impact corn yield. The research is extended on five locations in both Romania and Luxembourg handling wheat, corn and rapeseed crop simulation.
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