Advancing Fusarium Head Blight Detection in Wheat Crop: A Review and Future Directions to Sustainable Agriculture
Bibliographic record
Abstract
Fusarium head blight (FHB) is a significant disease affecting wheat, one of the most vital global crops, with severe implications for crop yield and food safety. Traditional FHB detection methods have limitations, highlighting the need for innovative and sustainable solutions. This review investigates the Fusarium species responsible for FHB, the disease’s life cycle, symptoms, and FHB mycotoxins affecting human and animal health. We focus on deep learning (DL) techniques for FHB detection that improve accuracy, resource efficiency, environmental sustainability, and error reduction compared to conventional methods. DL models, integrated with RGB and spectral imaging and enhanced by transfer learning and data augmentation, achieve high precision in detecting FHB across wheat varieties. Advancements in UAV-based RGB and spectral imaging paired with DL show promising results for early detection, reducing crop damage and lowering carbon emissions. However, data imbalance, background noise, and model overestimation persist. Optimization strategies and multi-modal approaches integrating imaging with environmental data have been proposed to enhance model robustness. This review underscores DL’s potential to improve FHB detection, crop yield, and sustainable agricultural practices. Future directions emphasize IoT integration, real-time low-power electronics, and blockchain for data security, addressing ethical concerns in labour and data privacy in agriculture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".