Abstract
In recent years, IoT sensors have enabled smart agriculture to grow rapidly with many compelling real-world applications. One such application is the case of smart irrigation. A particular interest exists in forecasting soil water potential to allow for the establishment of more efficient irrigation systems. Nonetheless, forecasting soil moisture is a complex task and depends on various information sources. Most existing work relies on local approaches, which are less effective at leveraging shared information across different data sources. Therefore, this paper presents a robust global approach for soil water potential forecasting, combining various environmental factors through the cutting-edge Temporal Fusion Transformer. Our proposed approach outperforms established baselines for forecasting soil water potential. As such, this work contributes to the growing body of research on data fusion in real-world applications.
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