I-Corps: Herd Management through Internet of Things Systems

Cornell University, Ithaca NY

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to improve milk production efficiency of dairy cows by measuring individual cow feed intake, a vital datapoint not typically commercially available in the industry. This datapoint allows dairy farmers to optimize diet formulation which would result in higher milk production per cow and reduce illnesses by being able to provide sufficient nutrients to support the cow's level of milk production. Using the data derived from this platform will also potentially improve the genetic pool of the herd. The commercial impact of the project has the potential to result in significant cost savings for dairy farmers by providing the vital benefits of early detection of the five most common diseases and feed costs savings. This I-Corps project uses custom-designed radio frequency identification sensor platform that is installed on feeding alleys and bedding of dairy barns. The sensors are designed to distinguish a cow from consuming or sifting through the feeds to provide a new and efficient way of monitoring dry matter intake on a per cow basis. The sensors also capture individual cow's time spent lying down (rumination time) and the number of visits to the feeding alley. All data gathered by the sensors are sent to the cloud continuously and analyzed by unique machine-learning algorithms to determine a range of behavioral events per individual cow, including feed intake monitoring to identify the most/least feed efficient cows. Decrease in feed intake over a period of time also signifies the onset of illnesses which can be detected prior to becoming clinical cases to reduce the use of antibiotics. 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.