Abstract:
Water scarcity, changing rainfall patterns, and increasing climate variability are creating new challenges for sustainable food production. Small community agriculture programs often lack access to sophisticated irrigation-management systems, despite having a strong need to conserve water and maintain healthy crops. FarmUnity, a Maryland-based nonprofit community agriculture organization, has developed and deployed an operational, multi-site AI-assisted smart irrigation system designed to improve irrigation decisions using real-time environmental and operational data. The system integrates soil-moisture measurements, local weather data, rainfall forecasts, automated irrigation valves, water-consumption monitoring, and software-based decision logic. Rather than relying solely on predetermined watering schedules, the system evaluates current conditions to determine whether irrigation is needed, should be delayed, or should be avoided. Watering activity, water consumption, and post-irrigation soil response are recorded to create a historical dataset for evaluating system performance and improving future decisions. This presentation will describe the system architecture and lessons learned from deployment in active community growing environments, including sensor reliability, connectivity, differences among growing zones, abnormal water-use readings, automation safeguards, and the importance of human oversight. The FarmUnity case study demonstrates how commercially available technologies can be integrated into an accessible framework for climate-resilient agriculture. The approach has potential application in community gardens, schools, nonprofits, urban agriculture, and other small scale food growing environments seeking to improve water efficiency while strengthening local food production and climate resilience.
Keywords: AI Assisted Irrigation, Climate Resilience, Water Efficiency, Community Agriculture, Precision Irrigation, Food Security.