AUTHOR=Castano-Duque Lina , Winzeler Edwin , Blackstock Joshua M. , Liu Cheng , Vergopolan Noemi , Focker Marlous , Barnett Kristin , Owens Phillip Ray , van der Fels-Klerx H. J. , Vaughan Martha M. , Rajasekaran Kanniah 

TITLE=Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1283127

DOI=10.3389/fmicb.2023.1283127

ISSN=1664-302X

ABSTRACT=Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties and land usage parameters were modeled to identify factors significantly contributing to outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked with soil properties.