Fungal Damage Detection in Wheat Using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging
Bibliographic record
Abstract
Healthy and fungal-damaged wheat kernels infected by the species of storage fungi, namely Penicillium spp., Aspergillus glaucus, and A. niger, were scanned using a short-wave near-infrared hyperspectral imaging system in the 700–1100 nm wavelength range and an area scan colour camera. A multivariate image analysis was used to reduce the dimensionality of the hyperspectral data and to select the significant wavelength using principal component analysis. Wavelength 870 nm, which corresponded to the highest factor loading of first principal component, was considered to be significant. Statistical and histogram features from the 870 nm wavelength image were selected and used as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis). From the colour images, a total of 179 features (123 colour and 56 textural) were extracted and the top features selected from these features were used as input to the statistical classifiers. The linear discriminant analysis classifier correctly classified 97.3–100.0% healthy and fungal-infected wheat kernels, using the combined hyperspectral image features and the top ten features selected from 179 colour and textural features of the colour images as input.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".