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Application of machine learning for predicting aflatoxin contamination risk in maize from Texas.

Kyung-Min Lee,Tim Herrman,Il-Kyu Kim,Prabha Vasudevan

Published 2026 in Food Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk Assessment

ABSTRACT

In this study, we developed models for predicting aflatoxin contamination in Texas maize samples. These models were trained on individual or median historical aflatoxin contamination records using machine learning algorithms. The weather, engineering feature (AFRI), remote sensing (NDVI), and soil property data available in the public domain were used as the main input variables for model development. The study utilised 9,694 maize samples collected from 973 Texas counties spanning the period from 2010 to 2023. The performance of all models was evaluated using either a 70/30 training/validation split (30% for validation) or a one-year validation dataset (year 2017). The evaluation was conducted at two threshold levels (5 μg/kg and 20 μg/kg) for high contamination events. The gradient boosting decision tree (GBDT) models developed on median data achieved the highest correct classification rate and balanced accuracy of nearly 99%, while the hierarchical logistic regression (HLR) models for individual data points showed the lowest overall accuracy. The GBDT models were highly effective at predicting the presence or absence of aflatoxin contamination, displaying exceptional sensitivity and specificity. The overall accuracy of other models was also acceptable, particularly at the 20 μg/kg threshold, with values ranging from 0.73 to 0.87. The developed models revealed that aflatoxin contamination was significantly associated with higher precipitation/AFRI/NDVI during the pre-flowering period, but was highly linked to lower precipitation/AFRI/NDVI during the flowering and post-flowering periods. Of the 25 soil properties investigated, moisture content, clay percentage, pH, and calcium carbonate were also significantly associated with aflatoxin contamination in maize. The developed models, along with the key influential predictors identified in this study, should be applicable and useful for growers and other relevant parties to take preventive action against aflatoxin contamination in maize produced in Texas.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    Food Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk Assessment

  • Publication date

    2026-02-26

  • Fields of study

    Agricultural and Food Sciences, Medicine, Environmental Science

  • Identifiers
    DOI 10.1080/19440049.2026.2632127PMID 41747161
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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