SBIR-STTR Award

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.

The broader impact of this Small Business Innovation Research (SBIR) Phase II project will result from the development of disruptive technologies that use drones and artificial intelligence to monitor livestock. Almost 75% of United States cattle are purchased by taking out bank loans, and lenders need to audit cattle inventories for collateral verification and appraisal purposes. Additionally, more than 140,000 ranchers need to monitor their herds and detect cattle illnesses before infections spread. The proposed technology will leverage aerial imaging and artificial intelligence to count cattle, characterize cattle weight, and diagnose cattle illnesses up to one week before clinical symptoms appear. The ability to count herds regularly will enable ranchers to discover cattle rustling issues early and provide banks with a reliable way to perform collateral verification on ranches and feedlots, ensuring that banks can continue extending livestock operations the loans that they need to survive. The ability to detect cattle illnesses early is expected to reduce cattle mortalities, the economic cost of antibiotic use, and possibly antibiotic resistance in humans. Ultimately, the proposed technology that will be developed for monitoring cattle promises to also transform the way that land and marine wildlife, fisheries, and endangered species are monitored. This Small Business Innovation Research (SBIR) Phase II project will provide cattlemen and bankers with an efficient way to detect and count cattle on pastures and ranches, estimate livestock weight, and identify ill cows before they spread infection further. Despite daily monitoring of cattle herds, small discrepancies and losses are undiscoverable, and bovine illnesses are often left undetected until they spread, infecting more cattle and requiring large-scale administration of antibiotics. Machine learning and image processing tools will be developed that (a) automatically analyze natural and thermal drone images to count cattle on multi-topography ranches and estimate livestock weight and (b) discriminate between healthy and ill cattle based on aerial radiometric imaging. The outcomes will be (1) a ready, drone-agnostic solution for counting cattle and estimating their weight and (2) a pilot-tested drone-and-software system for monitoring cattle health via radiometric imaging and notifying cattlemen about cows with suspected illness in real-time. 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.