Smart Agriculture Framework with Digital Twin: A Monitoring Model Based on Clustering and prediction of Multivariate Time Series
Year: 2024
Type: conference-paper
Abstract: 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.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4404742497