ArticleFrontiers in microbiology2021
Machine Learning for Predicting Mycotoxin Occurrence in Maize.
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Systematic Review of Soil Properties to Support Mycotoxin Model Development with In-Field Soil Sensing.Sensors (Basel, Switzerland) · 2026Pooled it
- The use of artificial intelligence to improve mycotoxin management: a review.Mycotoxin research · 2025Pooled it
- Pooled it
- An integrated adaptive framework for mycotoxin management in African cereals: evidence from a scoping review.Mycotoxin research · 2026Article
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Projected mycotoxin contamination in European wheat under future climate and socioeconomic scenarios.NPJ science of food · 2026Article
- Climate-stressed crop mycobiomes as pre-harvest sentinels of human mycotoxin exposure: an AI-enabled One Health framework linking dysbiosis, exposomics, and toxicopathology.Frontiers in systems biology · 2026Review
- Early warning predictions of contaminants in animal feed using machine learning.NPJ science of food · 2025Article
- Review
- AI and Biotechnology to Combat Aflatoxins: Future Directions for Modern Technologies in Reducing Aflatoxin Risk.Toxins · 2025Review
- Review
- New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods.Toxins · 2025Review
- Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms.Food chemistry: X · 2025Article
- Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.Journal of agricultural and food chemistry · 2025Article
- Evaluating Methods for Aflatoxin B1 Monitoring in Selected Food Crops Within Decentralized Agricultural Systems.Toxins · 2025Article
- Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models.Frontiers in microbiology · 2025Article
- Mycotoxins in Food: Cancer Risks and Strategies for Control.Foods (Basel, Switzerland) · 2024Review
- Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.Foods (Basel, Switzerland) · 2024Review
- Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence.NPJ science of food · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
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