Director, data science and information technology services
Accurate prediction of broiler bird weight distributions using machine learning
Estimating broiler chicken weight distributions across a flock using traditional linear regression and other mathematical models has been difficult to perform and the results are imprecise. As a result, most poultry operations rely on “standard” or generalized average weight distributions that don’t adjust well to changing factors. In this presentation, learn more about a system that uses telemetry data and machine learning to provide an accurate prediction of weight distributions.
Christopher Lee joined MTech Systems in January of 2003 and for over a decade has served in various management roles at MTech. He established a data science team starting in December 2018 while maintaining his IT responsibilities as director — data science and information technology services. The newly formed data science team has concentrated on providing new artificial intelligence solutions that can be integrated in MTech’s current and new product offerings. Predictive solutions include broiler weight distribution, hatchability percent, high condemnation alert and numerous other predictive models using machine learning. Before his days at MTech, Lee held several positions in management and consulting. Chris received his bachelor’s degree in economics from the University of Georgia. He continued to earn a master’s degree in information systems from Kennesaw State University.