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Gradient boosting machine learning model to predict aflatoxins in Iowa corn.

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Aflatoxin (AFL) contamination in corn can be predicted using a machine learning model incorporating weather, satellite, and soil data. This model aids in proactive hazard management for safer food and feed supplies.

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Aflatoxin (AFL) contamination in corn poses significant health risks due to its toxic and carcinogenic properties.
  • Ensuring food and feed safety in the US necessitates effective AFL mitigation strategies for this vital commodity.

Purpose of the Study:

  • To develop and evaluate an Iowa-centric predictive model for AFL contamination in corn.
  • To utilize historical data, meteorological, satellite, and soil properties for AFL risk assessment.

Main Methods:

  • Gradient Boosting Machine (GBM) learning was employed for AFL prediction.
  • Two AFL risk thresholds (20-ppb and 5-ppb) were evaluated using a 90%-10% training-to-testing ratio and independent validation.
  • Feature engineering was applied to identify key predictive variables.

Main Results:

  • The GBM model achieved high overall accuracy (96.77% for 20-ppb, 90.32% for 5-ppb) but showed low sensitivity for detecting high contamination events.
  • Satellite-derived vegetation index in August significantly improved end-of-season contamination prediction.
  • Aflatoxin Risk Indices (ARI) in May and July, latitude, and soil-saturated hydraulic conductivity (Ksat) were identified as influential factors.

Conclusions:

  • Predictive AFL models are practical for grain handling, enabling preventative rather than reactive mitigation.
  • Identifying annual AFL risk predictors is crucial for cost-effective hazard management and optimal corn crop utilization.