Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models.

ABSTRACT: Aflatoxins are carcinogenic and mutagenic mycotoxins that contaminate food and feed. The objective of our research is to predict aflatoxin outbreaks in Texas-grown maize using dynamic geospatial data from remote sensing satellites, soil properties data, and meteorological data by an ensemble of models. We developed three model pipelines: two included mechanistic models that use weekly aflatoxin risk indexes (ARIs) as inputs, and one included a weather-centric model; all three models incorporated soil properties as inputs. For the mechanistic-dependent models, ARIs were weighted based on a maize phenological model that used satellite-acquired normalized difference vegetation index (NDVI) data to predict maize planting dates for each growing season on a county basis. For aflatoxin outbreak p

SUBMITTER: Castano-Duque L 

PROVIDER: S-EPMC11919900 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature