I-Corps: Data-Driven Health and Location Monitoring of Livestock

I-Corps项目的更广泛影响/商业潜力是通过精确、自动化的畜群管理支持可持续农业的新型创新产品。由于肉牛在很大程度上是人工检查和管理的，预计将首先应用于肉牛。这是一个劳动密集型的过程，很容易出错，比如在牧场和牧场上找不到动物。缺乏商业上可行的技术来跟踪和监测相对较短范围以外的牛。这一创新直接解决了这一不足，通过位置跟踪和提供长距离单个动物的健康监测，使牛肉生产商能够节省管理时间和劳动力，避免损失，最大限度地提高畜群的生育能力和营养，并可持续地管理牧场以防止环境破坏。I-Corps项目开发了一个新的技术平台，该平台可生成高质量的地理空间数据，能够显著推进对畜牧业生产力和管理的科学理解。这项创新应用了物联网（IoT）通信和移动计算硬件的最新进展，创建了一个低功耗系统，可以收集、分析和传输动物健康和位置信息。这项创新建立在研究的基础上，研究证明了技术方法的可行性和设计路径，以及初步的动物测试，证明了传感器在牲畜非侵入性数据收集方面的功能。该平台有潜力确定用于疾病早期检测的生物标志物，量化外部压力源对牲畜生育力和生产力的影响，启用自动化/机器辅助放牧的工具和程序，并量化可持续放牧做法的生态效益。足够大的数据集将使评估动物健康状况的先进算法成为可能。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this I-Corps project are new and innovative products that support sustainable agriculture through precise, automated livestock herd management. Beef cattle is anticipated to be the initial application as beef cattle are largely inspected and managed manually. This is a labor-intensive process and can be prone to mistakes, as lose of tracking of animals in the pastures and rangelands. There is a lack of commercially viable technologies for tracking and monitoring cattle beyond a relatively short range. This innovation directly addresses this shortfall through location tracking and providess health monitoring of individual animals over long distances, enabling beef producers to save management time and labor, avoid losses, maximize herd fertility and nutrition, and sustainably manage rangelands to prevent environmental damage.This I-Corps project develops a new technology platform that generates high quality, geospatially enabled data with the ability to significantly advance the scientific understanding of livestock productivity and management. This innovation applies recent advances in internet of things (IoT) communications and mobile computing hardware to create a low-power system that collects, analyzes and transmits animal health and location information. This innovation builds upon research that demonstrated the feasibility and design path for the technical approach, and initial animal testing that demonstrated sensor functionality for non-invasive data collection from livestock. The platform has potential to determine biomarkers for early detection of disease, quantify the effect of external stressors on livestock fertility and productivity, enable tools and and procedures for automated/machine-assisted herding, and quantify the ecological benefit of sustainable grazing practices. Sufficiently large datasets will enable advanced algorithms for assessing animal health states.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.