ArticleFrontiers in microbiology2022
Gradient boosting and bayesian network machine learning models predict aflatoxin and fumonisin contamination of maize in Illinois - First USA case study.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 26 citations in OpenAlex.
- Climate-Driven Regulatory Plasticity Reshapes Mycotoxin Risk and Control in Food Systems.Comprehensive reviews in food science and food safety · 2026Review
- Projected mycotoxin contamination in European wheat under future climate and socioeconomic scenarios.NPJ science of food · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Mycotoxins and plant diseases in a changing climate: from pathogen ecology to smart surveillance and mitigation strategies.Frontiers in fungal biology · 2026Review
- Predicting aflatoxin MCurrent research in food science · 2026Article
- Article
- Application of Machine Learning in Food Safety Risk Assessment.Foods (Basel, Switzerland) · 2025Review
- Modeling the Role of Weather Patterns and Grain Quality in On-Farm Engulfments and Entrapments.Journal of agricultural safety and health · 2025Article
- Processing and Real-Time Monitoring Strategies of Aflatoxin Reduction in Pistachios: Innovative Nonthermal Methods, Advanced Biosensing Platforms, and AI-Based Predictive Approaches.Foods (Basel, Switzerland) · 2025Review
- Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models.Frontiers in microbiology · 2025Article
- Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence.NPJ science of food · 2024Article
- Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning.Frontiers in microbiology · 2023Article
- Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year.International journal of food science · 2023Article
- Gradient boosting machine learning model to predict aflatoxins in Iowa corn.Frontiers in microbiology · 2023Article
- Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycotoxin contamination of corn results in significant agroeconomic losses and poses serious health issues worldwide. This paper presents the first report utilizing machine learning and historical aflatoxin and fumonisin contamination levels in-order-to develop models that can confidently predict mycotoxin contamination of corn in Illinois, a major corn producing state in the USA. Historical monthly meteorological data from a 14-year period combined with corresponding aflatoxin and fumonisin contamination data from the State of Illinois were used to engineer input features that link weather, fungal growth, and aflatoxin production in combination with gradient boosting (GBM) and bayesian network (BN) modeling. The GBM and BN models developed can predict mycotoxin contamination with overall 94% accuracy. Analyses for aflatoxin and fumonisin with GBM showed that meteorological and satellite-acquired vegetative index data during March significantly influenced grain contamination at the end of the corn growing season. Prediction of high aflatoxin contamination levels was linked to high aflatoxin risk index in March/June/July, high vegetative index in March and low vegetative index in July. Correspondingly, high levels of fumonisin contamination were linked to high precipitation levels in February/March/September and high vegetative index in March. During corn flowering time in June, higher temperatures range increased prediction of high levels of fumonisin contamination, while high aflatoxin contamination levels were linked to high aflatoxin risk index. Meteorological events prior to corn planting in the field have high influence on predicting aflatoxin and fumonisin contamination levels at the end of the year. These early-year events detected by the models can directly assist farmers and stakeholders to make informed decisions to prevent mycotoxin contamination of Illinois grown corn.
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