Abstract:
The need for a knowledge graph-powered hub is multi-faceted. At its core, the knowledge graph offers a semantically rich structure that mimics the interconnectedness of real-world systems, allowing for a nuanced representation of data relationships. This facilitates the application of foundational AI research areas such as multi-modal spatiotemporal deep learning, explainable AI, and neuro-symbolic question answering. With these technologies, we can analyze and understand the function and malfunctions of agricultural systems, predict outcomes, and make recommendations that are transparent and justifiable.