Project Description: The goal of this project is developing new data analysis tools for streamlining the North Carolina sweetpotato industry. A nexus point of all crop value is located at sorting facilities, where produce is sorted into value-added categories. These physical characteristics will be linked to up-stream provenance data (field location, weather data, management practices) and down-stream value (consumer preferences, storage life, etc.) to develop new management practices that maximize value. Currently, our lab is developing aspects related to the back-end machine learning classification techniques for quantifying sweetpotato phenotypes.
Contributors: Dr. Samiul Haque, Hangjin Liu, Daniel Katowitz, Stephen Chang.
