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Related Experiment Video

Updated: Nov 8, 2025

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Published on: April 23, 2012

Machine Learning for Predicting Mycotoxin Occurrence in Maize.

Marco Camardo Leggieri¹, Marco Mazzoni¹, Paola Battilani¹

¹Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy.

Frontiers in Microbiology

|April 26, 2021

Summary

This summary is machine-generated.

Machine learning models accurately predict mycotoxin contamination in maize grain, considering weather and cropping systems. This approach improves upon traditional methods for forecasting aflatoxin B1 and fumonisins.

Keywords:

Aspergillus flavus Fusarium verticillioides aflatoxins cropping system deep learning fumonisins predictive models

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Published on: January 3, 2014

Area of Science:

Agricultural Science
Mycology
Data Science

Background:

Mycotoxin contamination in maize is influenced by weather, but cropping systems can mitigate this.
Previous research on cropping systems' role in mycotoxin contamination yielded inconclusive results.
Predictive modeling for mycotoxin contamination requires robust input variables.

Purpose of the Study:

To develop and validate machine learning models for predicting mycotoxin contamination in maize.
To integrate weather-based predictions and cropping system data for enhanced accuracy.
To assess the added value of machine learning over classical statistical approaches.

Main Methods:

Utilized a machine learning approach, specifically deep neural networks (DNNs).
Input variables included weather-based mechanistic model predictions (AFLA-maize, FER-maize) and cropping system factors.
Trained models on a 13-year dataset (2005-2018) of mycotoxin occurrence and cropping data from northern Italy.

Main Results:

Developed two DNN models to predict aflatoxin B1 (AFB1) and fumonisins (FBs) contamination in maize fields at harvest.
Both models achieved prediction accuracy greater than 75%.
Demonstrated superior predictive performance compared to AFLA-maize and FER-maize models and classical statistical methods.

Conclusions:

Machine learning models offer a valuable approach for predicting mycotoxin contamination in maize.
The developed models are robust, evidenced by the large dataset and strong statistical scores.
This predictive capability can aid in managing mycotoxin risks in agricultural systems.