Machine Learning for Predicting Mycotoxin Occurrence in Maize

Meteorological conditions are the main driving variables for mycotoxin-producing fungi and the resulting contamination in maize grain, but the cropping system used can mitigate this weather impact considerably. Several researchers have investigated cropping operations' role in mycotoxin contamination, but these findings were inconclusive, precluding their use in predictive modeling. In this study a machine learning (ML) approach was considered, which included weather-based mechanistic model predictions for AFLA-maize and FER-maize [predicting aflatoxin B-1 (AFB(1)) and fumonisins (FBs), respectively], and cropping system factors as the input variables. The occurrence of AFB(1) and FBs in maize fields was recorded, and their corresponding cropping system data collected, over the years 2005-2018 in northern Italy. Two deep neural network (DNN) models were trained to predict, at harvest, which maize fields were contaminated beyond the legal limit with AFB(1) and FBs. Both models reached an accuracy >75% demonstrating the ML approach added value with respect to classical statistical approaches (i.e., simple or multiple linear regression models). The improved predictive performance compared with that obtained for AFLA-maize and FER-maize was clearly demonstrated. This coupled to the large data set used, comprising a 13-year time series, and the good results for the statistical scores applied, together confirmed the robustness of the models developed here.

Automated summary

This study utilized a machine learning approach to predict mycotoxin contamination in maize fields, achieving an accuracy of over 75% which outperformed traditional statistical methods. The models developed here were shown to be robust, thanks to the large data set and good statistical scores applied.