SBIR Phase I: Artificial intelligence powered optical spectrometer technology for farm-level milk testing

SBIR 第一阶段：用于农场级牛奶检测的人工智能驱动的光学光谱仪技术

• 批准号: 2125274
• 负责人: Anshuman Das
• 金额: $ 25.59万
• 依托单位: LABBY INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125274&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; intelligence; powered

This Small Business Innovation Research Phase I project addresses improved care of cattle. Mastitis is a common udder infection that impacts both the quality and quantity of milk and causes severe losses for farmers if not detected early. As a result, the dairy industry loses $32 billion each year, even though mastitis is treatable. This project enables farm-level milk quality testing. Cows with mastitis produce cellular indicators that can be measured in milk, along with fat and protein content. The proposed technology measures these quantities at the point of milking. Dairy products are a major source of nutrition in many countries, and frequent testing improves revenues and minimizes both the costs related to mastitis as well as the use of antibiotics through early detection. This project integrates mobile optical spectrometer technology and software analytics to test milk quality indicators, including fat, protein, and somatic cell counts, at the dairy farm. The envisioned solution is a portable device and an in-line unit integrated into milking systems to indicate milk quality at the level of the individual cow. A mobile application supported by a cloud back-end constitutes the software. The app provides test results, whereas the cloud interface generates individual and herd-level analytics. This project addresses: high prediction accuracy (95%) of milk fat, protein and somatic cell counts using ultraviolet-visible mobile spectroscopy; an artificial intelligence framework to predict the occurrence and impact of infections; and detection of milk components, such as lactose, using near-infrared spectroscopy. A large volume of training data will be collected with partner farms and laboratories. In-line systems will be installed on site to automate milk sample collection and testing. After sufficient data are obtained, a longitudinal analytics framework will be developed to predict long-term herd performance.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.

这个小企业创新研究第一阶段项目致力于改善牛的护理。 乳腺炎是一种常见的乳房感染，会影响牛奶的质量和数量，如果不及早发现，会给农民造成严重损失。 因此，尽管乳腺炎是可以治疗的，乳制品行业每年仍损失 320 亿美元。该项目实现了农场级牛奶质量检测。患有乳腺炎的奶牛会产生可以在牛奶中测量的细胞指标以及脂肪和蛋白质含量。所提出的技术在挤奶时测量这些数量。 乳制品是许多国家的主要营养来源，频繁的检测可以提高收入，并通过早期检测最大限度地减少与乳腺炎相关的成本以及抗生素的使用。该项目集成了移动光学光谱仪技术和软件分析，以测试奶牛场的牛奶质量指标，包括脂肪、蛋白质和体细胞计数。设想的解决方案是集成到挤奶系统中的便携式设备和在线装置，以指示每头奶牛的牛奶质量。由云后端支持的移动应用程序构成了该软件。该应用程序提供测试结果，而云界面则生成个体和群体级别的分析。该项目致力于：使用紫外-可见移动光谱仪对乳脂、蛋白质和体细胞计数进行高预测准确度（95%）；预测感染发生和影响的人工智能框架；使用近红外光谱检测乳糖等牛奶成分。将与合作伙伴农场和实验室收集大量培训数据。现场将安装在线系统，以自动收集和测试牛奶样品。获得足够的数据后，将开发纵向分析框架来预测长期牛群表现。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。