SBIR Phase I: Artificial intelligence powered optical spectrometer technology for farm-level milk testing
SBIR Phase I: Artificial intelligence powered optical spectrometer technology for farm-level milk testing
批准号:
2125274
负责人:
Anshuman Das
金额:
$25.59万
依托单位:
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
项目状态:
已结题
起止时间:
2021-09-01 至 2022-05-31
中文摘要
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英文摘要
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.
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