Explores the concept and application of Digital Twins in agriculture, focusing on how satellite remote sensing, ground-based observations, and meteorological data can be combined using AI and machine learning models. The aim is to create digital replicas of agricultural systems that help monitor and predict soil moisture, crop health, and irrigation needs.
What will you obtain?
You will gain an understanding of how to build and use Digital Twin systems for agricultural applications. This includes learning about satellite platforms, vegetation and soil indices, data integration methods, forecasting tools like LSTM models, and practical case studies such as the AI4WATER project. You'll also acquire skills to interpret complex data and use it for more efficient irrigation planning.
What is it for?
This knowledge is intended to support more sustainable and precise farming practices. By using Digital Twins, farmers, researchers, and technologists can optimize water usage, reduce resource waste, and increase crop productivity through informed, data-driven decisions.
