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

SBIR 第二阶段：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: 2233881
• 负责人: Julia Somerdin
• 金额: $ 88.6万
• 依托单位: LABBY INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233881&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Artificial; intelligence

This Small Business Innovation Research (SBIR) Phase II project develops a cow's milk testing system using mobile spectroscopy and machine learning to provide rapid and automatic milk testing. The team aims to address the annual $32 billion global loss from bovine mastitis, an udder disease, due to the lack of farm-level early detection technology. The project helps farmers detect mastitis early, allowing them to increase farm operation efficiency, lessen the use of antibiotics, improve animal health, and reduce greenhouse gas emissions. The expanding herd size of dairy farms, shortage of labor, and rising dairy consumption across the globe are driving growth in the global livestock monitoring market which is expected to reach $19 billion by 2030. Globally, the total addressable market size is estimated at $12 billion. The technology under development in this Phase II project will enable precision dairy production by bringing cutting-edge technology to the farm and creating opportunities to attract and retain a new generation of dairy workers. The project’s mission is to support the dairy industry in delivering the best quality milk in an efficient and sustainable way.The intellectual merit of this project involves on-farm, real-time, and reliable testing of milk components such as somatic cell counts, fat, and protein, using mobile optical spectrometer technology that is controlled by physics-informed machine learning. An improved industrial design of the inline milk testing unit will be developed that is tailored for robotic dairy farms. Additionally, an embedded sampler prototype will be tested in conventional dairy farms to fully automate milk sampling and testing with the goal of developing a universal device that works for most parlor configurations. The operating wavelength range of the devices will be broadened using near-infrared and shortwave-infrared chips, which will not only increase the accuracy of fat and protein measurements but will also expand the testing to components such as lactose and milk urea nitrogen. From the data perspective, time-series measurements of somatic cell counts will be combined with historical herd-level and individual cow-level data such as days-in-milking, lactation, and yield, to build predictive models for mastitis and milk yield. Finally, optical signals such as fluorescence will be used to ascertain the presence of harmful pathogens in milk, to aid in the diagnosis of infections and prevent contaminated milk from entering the supply chain.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.

这个小型企业创新研究 (SBIR) 第二阶段项目开发了一种牛奶检测系统，利用移动光谱和机器学习来提供快速、自动的牛奶检测。该团队旨在解决因牛乳腺炎（一种乳房疾病）而造成的每年 320 亿美元的全球损失。由于缺乏农场层面的早期检测技术，该项目可以帮助农民及早发现乳腺炎，从而提高农场运营效率，减少抗生素的使用，改善动物健康，并减少奶牛畜群规模的扩大。农场、劳动力短缺和全球乳制品消费量的增加正在推动全球牲畜监测市场的增长，预计到 2030 年将达到 190 亿美元。全球范围内，潜在市场总规模估计为 120 亿美元。该二期项目将通过为农场带来尖端技术并创造吸引和留住新一代乳制品工人的机会来实现精准乳制品生产。该项目的使命是支持乳制品行业高效地提供最优质的牛奶。和可持续的方式。知识分子该项目的优点包括使用由物理信息机器学习控制的移动光谱仪技术，对牛奶成分（如体细胞计数、脂肪和蛋白质）进行实时、可靠的测试。此外，还将开发专为机器人奶牛场定制的在线牛奶测试装置，并将在传统奶牛场中测试嵌入式采样器原型，以实现全自动牛奶采样和测试，目标是开发适用于大多数奶牛场配置的通用设备。 .工作波长范围设备将使用近红外和短波红外芯片进行拓宽，这不仅会提高脂肪和蛋白质测量的准确性，还会将测试范围扩大到乳糖和牛奶尿素氮等成分。体细胞计数的测量结果将与历史牛群水平和个体奶牛水平数据（例如挤奶天数、哺乳期和产量）相结合，以建立乳腺炎和产奶量的预测模型，最后，荧光等光学信号将被结合起来。是用于确定牛奶中是否存在有害病原体，帮助诊断感染并防止受污染的牛奶进入供应链。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评估进行评估，认为值得支持影响审查标准。