SAS Partners with NC Universities on Agricultural Sensor Project to Combat Flooding and Soil Salinity

Key Takeaways

• SAS, NC State, and ECU pilot sensors in Hyde County farms.
• Sensors track water depth, moisture, salinity, feeding SAS IoT analytics.
• Real-time data predicts floods, helps mitigate $111B agricultural impact.
• Project leverages SAS Viya‑based Ag Analytics Platform developed with NC A&T.
• Findings to be presented at ClimateTech Connect, boosting ag‑tech visibility.

Pulse Analysis

North Carolina’s coastal farms face mounting pressure from extreme weather events, with recent hurricanes and tropical storms causing severe crop losses. The region’s agricultural output, valued at roughly $111 billion annually, is especially vulnerable to flooding and saltwater intrusion, which degrade soil health and damage equipment. As climate variability intensifies, growers are seeking precision tools that can provide early warnings and actionable insights, turning raw environmental data into strategic decisions.

SAS’s collaboration with NC State, the Plant Sciences Initiative, and East Carolina University introduces a low‑cost sensor suite that continuously measures water depth, soil moisture and salinity. These data streams feed directly into SAS Analytics for IoT, built on the robust SAS Viya platform, enabling predictive modeling of flood risk and salinity spread. By integrating university research expertise with enterprise‑grade analytics, the pilot offers farmers a scalable solution that can be expanded across the state’s 1.3 million acres of vulnerable farmland. Early adopters in Hyde County report more precise irrigation scheduling and the ability to pre‑emptively protect fields before floodwaters arrive.

The broader implications extend beyond regional resilience. Demonstrating a successful public‑private partnership, the project positions SAS as a leader in ag‑tech innovation and showcases the commercial viability of IoT‑driven climate adaptation. As the findings are presented at ClimateTech Connect, investors and policymakers will likely view the model as a template for other flood‑prone agricultural zones nationwide. Continued refinement of the analytics platform could spur new services, such as insurance risk assessments and supply‑chain forecasting, further embedding data‑centric strategies into modern farming.