SBIR Phase I: Sonar Arrays for Maximizing Aquaculture Yields

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will be a method to improve shrimp farming using sonar and analytical software. The $45 billion shrimp industry operates with little understanding of how many shrimp are in a pond. Farmers take a "best guess" type of approach using incomplete and often inaccurate information to make daily decisions that results in poor feed to biomass conversion rates, suboptimal harvest timing and a variety of supply chain traceability and management issues. The goal of this project is to develop a noninvasive sonar-based assessment system to improve the accuracy and convenience of estimating shrimp abundance and growth in-situ. The proposed system will provide the farmer with actionable data that lead to the following value propositions: reducing the feed conversion ratio (feed titration), increasing profitability (determine harvest time), accounting controls (farmers with multiple sites controlling production), maximizing growth conditions (stocking density), reducing risk factors (biosecurity), and taking emergency action (crop triage). This SBIR Phase I project proposes to develop a sensor and analytics platform that would allow for estimation of shrimp biomass and behavior in real-time, informing decision making and best practices in shrimp aquaculture. By optimizing a series of networked underwater sonar sensors, it will be possible to non-invasively capture the species-specific abundance of shrimp in the challenging of environment of a highly aerated farm. In addition, algorithms will produce improved estimates of abundance, size, growth and behavioral abnormalities to the end user and informing go/no-go decisions as well as reducing sunk costs associated with uncertainty-based inefficiencies. Making "big data" available to the farmer, the buyers, as well as the scientific community. The controlled setting aquaculture provides for research is an untapped resource because the data are not standardized across ponds, regions or countries. The development of the proposed technology would be key in disseminating important research to the public via peer-reviewed publications on sensor optimization, animal growth, production, behavior and disease. Though the scope of this research and this product focuses on the shrimp sector, the model can be extended to other cultured species and applications (i.e., salmon and tilapia). 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.