Detection of Fungal Infection in Canola using Near-Infrared Hyperspectral Imaging
Bibliographic record
Abstract
Near-infrared (NIR) hyperspectral imaging was used to detect the presence of fungal infection in stored canola. Artificially fungal infected (Aspergillus glaucus group) canola was subjected to single kernel imaging every two weeks after incubation using an NIR imaging system in the wavelength range of 1000 to 1600 nm at 60 evenly distributed wavelengths. Three wavelengths 1100, 1230 and 1300 nm were identified as significant wavelengths and used in the analysis. Statistical discriminant classifiers (Linear and Quadratic) were used to classify healthy, two–, four–, six–, eight–, and ten–week fungal incubated samples. The linear and quadratic statistical classifiers gave maximum accuracy of 99% for healthy samples and 100% for fungal infected samples at later stages of infection levels and 90 to 95% for the first four weeks of A. glaucus infected samples.
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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".