How math helps to protect crops from invasive disease

"Our research focuses on predicting aflatoxin outbreaks in Texas using remote sensing satellites, soil properties and meteorological data," said coauthor Angela Avila, a postdoctoral fellow in mathematics at UTA. "One of the key challenges is that contamination can be present with no visible signs of fungal infection. This makes early risk prediction especially important for allowing targeted prevention and mitigation strategies."

Aflatoxins are toxic compounds produced by certain fungi in the mycotoxin family and are commonly found on crops such as corn (maize) and some nuts. They are carcinogenic and can pose serious health risks to humans and animals.

The research team included Jianzhong Su, professor and chair of UT Arlington's Department of Mathematics and Dr. Avila's former doctoral mentor. Together, they developed the aflatoxin risk index (ARI) and applied multiple machine learning methods to predict aflatoxin outbreaks in Texas. ARI is a predictive model that measures the cumulative risk of contamination during crop development.

"My main contribution was calculating historical planting dates for each county in Texas using time-series satellite imagery," Avila said. "Because maize is most susceptible to aflatoxin contamination at specific growth stages, having precise planting dates is critical. My contributions for planting date estimations significantly improved our risk assessment, enhancing the accuracy of our machine learning models by 20% to 30%."

"As part of her contributions to our mycotoxin research, Dr. Avila integrated a new input. She used the normalized difference vegetation index, acquired from satellite imagery, to predict planting times," said Lina Castano-Duque, lead author of the study in Frontiers in Microbiology and plant pathologist at the USDA Agricultural Research Service Southern Regional Research Center in New Orleans. "She will continue growing her model to apply it to the rest of the U.S."

Avila noted that the study has wide-reaching implications for farmers, processors and consumers, as mycotoxin contamination leads to billions of dollars in economic losses each year.

"Our research will allow farmers to make informed decisions to implement effective mitigation strategies, helping protect crops, food security, sustainability and economic stability," Avila said.

"This cutting-edge research will revolutionize the management of mycotoxin contamination in corn, addressing its associated challenges," Dr. Castano-Duque said. "Farmers will benefit from expert guidance on the risk levels of mycotoxin contamination that will aid in future crop selection and the ability to adapt input variables, such as fungicide and biocontrol application, as needed."

Support for this research was provided by the U.S. Department of Agriculture's Agricultural Research Service.