This project focuses on providing basic knowledge about aflatoxin vulnerability within a model crop food system, and utilizing the application of sensing methods for both crop stress and aflatoxin contamination. Vulnerability of agroecosystems to aflatoxin is a major problem worldwide, and in the U.S., represents an economic threat from high costs associated with testing and lost trade when outbreaks occur – events predicted to become increasingly common with climate variability.
Our team includes engineers, agronomists, breeders, physiologists, and data scientists, utilizing an integrated systems approach, employing modeling, new sensing technologies, and data mining for assessing risk at vulnerability points. We have identified peanut as an appropriate crop model, and our methods include: developing an aflatoxin regional risk model with launch on our team’s existing web/smartphone platform; utilizing our team’s Biogenic Volatile Organic Compound (BVOC) sensing system to detect crop drought stress (a major factor contributing to aflatoxin risk); utilizing both BVOC and a hyperspectral imaging and analysis algorithms our team has developed to monitor aflatoxin in peanut samples at harvest and removal from storage; and utilizing data mining and path analysis to fully assess system aflatoxin risk. This research will lead to the development of novel modeling, sensing, and data synthesis approaches to evaluate aflatoxin risk in a production system, a factor critically impacting the sustainability of U.S. production systems.