Excellence in Research: Empowering Sustainable Aquaponics with AI and Robotics for Enhanced Resource Efficiency

This project will advance food production by developing smarter aquaponics systems that use advanced sensing, data analysis, and automation technologies to improve the efficient use of water, energy, and nutrients. Aquaponics combines fish production and plant cultivation in a single integrated system and has considerable potential to support local food production and security. However, widespread adoption remains limited by high operating costs, technical complexity, and system instability. Led by Virginia State University in partnership with Virginia Polytechnic Institute and State University and Drexel University, this four-year research activity will develop practical approaches to improve the reliability, affordability, and accessibility of aquaponics systems. The project will engage students, educators, producers, and community members through research experiences, workforce development activities, programming, and community outreach. Expected outcomes include more efficient food production systems, expanded educational opportunities, increased community engagement in science and technology, and strengthened capacity to support advanced agriculture in communities with limited access to educational, technical, and economic resources. The project will contribute to the further development of an advanced agricultural workforce prepared to address future challenges in food production and agricultural innovation while leveraging emerging technology areas, including artificial intelligence and advanced manufacturing. This research activity aims to investigate how advanced sensing technologies, machine learning methods, hyperspectral imaging, and robotic monitoring systems can be integrated to improve resource efficiency in aquaponics production systems. The project will establish comprehensive monitoring networks to measure energy use, water quality, nutrient dynamics, plant performance, and fish health within controlled aquaponics environments. Data-driven optimization methods will be developed to identify operating conditions that reduce energy consumption, increase water recovery, improve nutrient retention, and maintain biological stability. Predictive models will be created to detect system failures and water quality degradation before major losses occur, enabling timely interventions that improve system reliability. The research will also evaluate standardized aquaponics designs through economic analysis and life-cycle assessment to determine practical pathways for community-scale adoption. Educational and workforce development activities will integrate research experiences for undergraduate, graduate, and pre-college students while expanding outreach to agricultural stakeholders and local communities. Project outcomes will advance knowledge in advanced food systems, intelligent agricultural technologies, controlled environment agriculture, and interdisciplinary workforce preparation. Research products, including validated methodologies, educational resources, extension materials, and openly accessible computational tools to advance the national competitiveness in sensing technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.