Detection of Fusarium Damaged Kernels in Wheat Using Deep Semi-Supervised Learning on a Novel WheatSeedBelt Dataset
Bibliographic record
Abstract
Fusarium head blight, caused by Fusarium spp., is a destructive disease of wheat worldwide. Fusarium damaged kernels (FDKs) significantly reduce grain yield and quality. Thus, FDK detection is a priority for wheat breeders seeking to develop high-grain quality and FDK-resistant wheat cultivars. However, traditional FDK measurement methods are time-consuming, labor-intensive, and of variable accuracy. Image-based phenotyping methods have the potential to efficiently detect FDK, but are challenging to develop due to the lack of large-scale damage-annotated wheat kernel datasets. Addressing this issue, we introduced WheatSeedBelt, a high-resolution large-scale dataset including 40,420 close-up top- and side-view single-kernel images of 268 wheat varieties with kernel damage annotations. Utilizing this dataset, we developed an image-processing pipeline to efficiently process images and extract the representative features for machine and deep-learning purposes. We also conducted three experiments on the dataset using pretraining and semi-supervised fine-tuning phases to classify wheat kernels into healthy, unhealthy but non-FDK, and FDK affected. Our best models achieved an F1-score of 84.29% for the Healthy-Unhealthy (including FDKs) task, 56.35% for the binary FDK-nonFDK, and 68.30% for the 3-class task (Healthy, Unhealthy, and FDK). We also conducted an inter-rater reliability study, which indicated that human experts do not outperform our model in FDK prediction, providing evidence that visual classification of FDK from RGB images is a challenging task.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".