SBIR Phase I: COWculator: Automated Cattle Counting and Bovine Temperature Screening from Aerial Feedlot Images

SBIR 第一阶段：COWculator：根据饲养场航空图像进行自动牛计数和牛温度筛查

• 批准号: 1913609
• 负责人: Shoshana Ginsburg
• 金额: $ 22.5万
• 依托单位: IMAGINAG TECH, 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 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913609&HistoricalAwards=false
• 关键词: SBIR; Phase; COWculator; Automated; Cattle

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a quick, easy, and accurate way to count cattle and detect bovine illnesses on feedlots and ranches via Unmanned Aerial Vehicles (UAVs). Current methods for counting cattle are extremely time-consuming or inaccurate, and sometimes both. Additionally, bovine illnesses are often diagnosed too late, leading to 50% of cattle mortalities on feedlots and yielding a $1.9 billion economic loss to the cattle industry. The proposed technology will leverage aerial images to (a) count cattle accurately and efficiently and (b) identify ill cows up to one week before clinical symptoms appear without the need to install expensive health-monitoring equipment on each cow. Ultimately, the proposed technology promises to more broadly impact the way wildlife and endangered species are tracked by automating wildlife counting on aerial images. This Small Business Innovation Research (SBIR) Phase I project proposes to develop an imaging-based solution for feedlot accountants, nutritionists, and auditors to monitor cattle. The project will leverage aerial photos of feedlot pens to automatically count all cattle breeds - regardless of season and ground conditions - using a combination of deep learning and traditional image processing tools. Additionally, this project will leverage aerial thermography to measure bovine temperatures; machine learning tools will be developed to differentiate between elevated body temperatures associated with illness and those associated with normal confounding factors. The goals of this Phase I project are to develop and fully validate the technology for cattle counting on feedlots and to establish the technical feasibility of leveraging aerial thermographic imaging for prediction of cattle health.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）第一阶段项目的更广泛的影响/商业潜力将来自于开发一种快速，简单和准确的方法来通过无人机（UAV）在饲养场和牧场上计数牛和检测牛疾病。 目前计算牛的方法要么非常耗时，要么不准确，有时两者兼而有之。 此外，牛的疾病往往诊断得太晚，导致饲养场50%的牛死亡，给养牛业造成19亿美元的经济损失。 拟议的技术将利用航空图像（a）准确有效地对牛进行计数，（B）在临床症状出现前一周内识别患病牛，而无需在每头牛身上安装昂贵的健康监测设备。 最终，拟议中的技术有望通过自动化航空图像上的野生动物计数来更广泛地影响野生动物和濒危物种的跟踪方式。 这个小型企业创新研究（SBIR）第一阶段项目建议为饲养场会计师，营养师和审计员开发一种基于成像的解决方案来监测牛。 该项目将利用饲养场围栏的航拍照片，使用深度学习和传统图像处理工具相结合，自动计算所有牛品种，无论季节和地面条件如何。 此外，该项目将利用航空热成像技术测量牛的温度;将开发机器学习工具，以区分与疾病相关的体温升高和与正常混杂因素相关的体温升高。 该项目第一阶段的目标是开发和全面验证饲养场牛计数技术，并建立利用航空热成像预测牛健康的技术可行性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。