Regional prediction of deoxynivalenol contamination in spring oats in Sweden using machine learning

Abstract Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. The region, year, spring oat variety, type of cultivation (organic or not) and if the oat is intended for feed or food - was used as input to predict DON contamination for entries into classes of low (< 500 µg/kg), medium (≥ 500 µg/kg, and < 1000 µg/kg), and high (≥ 1000 µg/kg). A random forest (RF) algorithm was applied to train the models. Results showed that: 1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72, over the years 2012-2019, and above 0.90 in the years 2016-2017, however not for individual years not included in the training of the models (external validation); 2) good predictions could already be made in June but using weather variables in the full growing season could improve the model’s robustness; 3) weather variables were the most important for predicting DON contamination, but adding agronomical and site-specific factors to weather variables as model inputs could improve the overall model performance; 4) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest. In future studies, it might be of interest to explore whether including data for other agronomic variables, such as fertilization, irrigation, and pest control, as well as satellite image data could further improve the model performance.