This project aims to approach seed placement analysis by developing a computer vision (CV) system with embedded artificial intelligence (AI) and Knowledge Graphs (KGs). Current seeding technologies rely on manual methods to evaluate seed placement, which limits efficiency and insights into its impact on plant health, crop yield, and disease management. By integrating CV-AI systems, this project will capture real-time data on seed placement, trench characteristics, plant health, and weed diversity. The KGs will process this data to provide predictive models that optimize yield, sustainability, and innovation. The initiative will position K-State as a leader in advanced agricultural technology, driving innovation and supporting food security efforts in Kansas.