Smart Course Management System: AI-based turf diagnostics and data-driven operations

In golf-course and large green-infrastructure operations, there was no integrated control system capable of monitoring wide outdoor areas and analyzing turf conditions in real time. That left vegetation management dependent on the subjective experience of field staff, causing repetitive overuse of chemical treatments and fertilization regardless of actual conditions. Historical records were also fragmented across handwritten and document-based logs, which made scientific optimization of growing conditions difficult.

To close that wide-area monitoring gap, high-resolution multispectral drone imagery was analyzed with Vision AI to identify disease zones and classify likely conditions automatically. Soil sensor data for moisture, temperature, and nutrients was integrated to support treatment recommendations at a detailed grid level rather than over whole holes. The broader field process, including equipment condition and material inventory, was turned into a digital pipeline, while domain knowledge was organized into a standardized database for mobile synchronization across field teams.

The result was an early-warning capability that detected turf disease and growth anomalies across large infrastructure environments at a level above 95%, helping teams secure response golden time. Zone-level precision treatment reduced unnecessary chemical usage and lowered material and operating cost by more than 25%. At the same time, full digitization of maintenance activity and time-series records made it possible to maintain continuity even during staff turnover, establishing a standard model for scientific smart-green operations.