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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".