SBIR Phase I: Sonar Arrays for Maximizing Aquaculture Yields

SBIR 第一阶段：用于最大化水产养殖产量的声纳阵列

• 批准号: 1913107
• 负责人: Suzan Shahrestani
• 金额: $ 22.5万
• 依托单位: MINNOWTECH LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913107&HistoricalAwards=false
• 关键词: SBIR; Phase; Sonar; Arrays; Maximizing

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.

这一小型企业创新研究(SBIR)项目的更广泛影响/商业潜力将是利用声纳和分析软件改善对虾养殖的方法。价值450亿美元的虾业在运营时，几乎不知道一个池塘里有多少虾。农户采用“最佳猜测”方式，利用不完整且往往不准确的信息进行日常决策，导致饲料到生物质的转化率不佳、收获时机不佳以及各种供应链可追溯性和管理问题。本项目的目标是开发一种基于声纳的非侵入性评估系统，以提高现场估计对虾丰度和生长的准确性和便利性。拟议的系统将为农民提供可操作的数据，这些数据将导致以下价值主张：降低饲料转化率(饲料滴定)，提高盈利能力(确定收获时间)，会计控制(多个地点控制生产的农民)，最大化生长条件(饲养密度)，降低风险因素(生物安全)，以及采取紧急行动(作物分类)。SBIR第一阶段项目建议开发一个传感器和分析平台，以便实时估计对虾的生物量和行为，为对虾水产养殖的决策和最佳实践提供信息。通过优化一系列网络化的水下声纳传感器，将有可能在高度充气的养殖场环境的挑战中，非侵入性地捕获特定物种的对虾数量。此外，算法将改进对最终用户的丰度、大小、增长和行为异常的估计，并为进行/不进行决策提供信息，以及降低与基于不确定性的低效相关的沉没成本。让农民、买家和科学界都能获得“大数据”。水产养殖为研究提供的受控环境是一种未开发的资源，因为池塘、区域或国家的数据没有标准化。拟议技术的开发将是通过关于传感器优化、动物生长、生产、行为和疾病的同行评议出版物向公众传播重要研究的关键。虽然这项研究和这一产品的范围集中在对虾领域，但该模型可以扩展到其他养殖物种和应用(即鲑鱼和罗非鱼)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。