AI-Augmented Systematic Review of Remote Sensing and Predictive Modelling for Mycotoxin Risk Monitoring in Cereal Crops Across Central and Balkan Europe.
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| Title: | AI-Augmented Systematic Review of Remote Sensing and Predictive Modelling for Mycotoxin Risk Monitoring in Cereal Crops Across Central and Balkan Europe. |
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| Authors: | Radócz Jr., László1 (AUTHOR), Nagy, Attila1,2 (AUTHOR) attilanagy@agr.unideb.hu, Szőllősi, Nikolett1 (AUTHOR), Kiss, Nikolett Éva1,2 (AUTHOR), Szabó, Andrea1 (AUTHOR), Tamás, János1 (AUTHOR), Siphiwe, Nxumalo Gift1 (AUTHOR), Radócz, László2 (AUTHOR) |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 13, p2063. 37p. |
| Subjects: | Multispectral imaging, Mycotoxins, Evidence synthesis, Prediction models, Deep learning, Crops, Remote sensing |
| Geographic Terms: | Balkan Peninsula, Europe |
| Abstract: | Highlights: What are the main findings? The research corpus expanded 56-fold between 2010 and 2025, with deep learning becoming the dominant approach and reaching 60% share of studies by 2025. Satellite multispectral imaging is the primary platform used in 91.7% of studies, achieving predictive performances of R2 = 0.74–0.80 for aflatoxin B1 and deoxynivalenol prediction in Central-Balkan Europe maize and wheat. What are the implications of the main findings? A five-percentage-point CBE–global performance gap is largely consistent with differences in sample size and multi-toxin design constraints rather than algorithmic access, as evidenced by comparable algorithm diversity scores across both groups. Significant geographic research gaps and a lack of data on T-2/HT-2 toxins in the Balkan region highlight the urgent need for harmonised monitoring infrastructures and climate-integrated EWS (early-warning systems). Mycotoxin contamination of cereal crops poses escalating food safety risks across the Central and Balkan European (CBE) corridor under climate change, yet no PRISMA 2020-compliant synthesis of remote sensing (RS) and machine learning (ML) evidence for this region exists. We conducted an AI-augmented systematic review applying a four-stage automated pipeline—PICO domain scoring, SBERT semantic deduplication, and Thompson-sampling reinforcement learning—to 36,038 corpus records (2010–2025), yielding 156 included studies (inter-rater κ = 0.81 (95% CI: 0.74–0.88)). Logistic growth modelling identified a 56-fold corpus expansion with inflection at t0 = 2024.8 (R2 = 0.981). Satellite multispectral imaging dominated the literature (91.7% of studies); random forest and gradient boosting models achieved R2 = 0.74–0.80 for aflatoxin B1 and deoxynivalenol prediction in CBE maize and wheat when integrating vegetation indices, land surface temperature, and precipitation covariates. Deep learning surpassed classical ML in annual study count from 2021, reaching ~60% relative share by 2025, though the performance advantage narrows at field scale relative to laboratory hyperspectral benchmarks (98–99% accuracy). A five-percentage-point CBE–global performance gap is largely consistent with differences in sample size and multi-toxin design scope rather than algorithmic access. The country × mycotoxin gap matrix identifies zero eligible studies for four CBE nations and for T-2/HT-2 toxins across the Balkan states. Climate-driven satellite mycotoxin prediction emerges as the field's active research frontier. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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