I-Corps: Data-Driven Health and Location Monitoring of Livestock
I-Corps: Data-Driven Health and Location Monitoring of Livestock
批准号:
1924776
负责人:
Delia Valles-Rosales
金额:
$5.0万
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
项目状态:
已结题
起止时间:
2019-06-15 至 2020-03-31
中文摘要
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英文摘要
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.
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