Comparison of soft X-rays and NIR spectroscopy to detect insect infestations in grain
Bibliographic record
Abstract
One of the challenges that need to be addressed to automate grain inspection is the machinedetection of insect infestations in grains. In this study, the soft X-ray and NIR spectroscopy methods to detectinsect infestations were evaluated for their potential for real-time application. Infested wheat kernels wereprepared by artificially infesting Canada Western Red Spring wheat by Sitophilus oryzae adults. Single kernelsof wheat uninfested and infested by larvae, pupae, and adults of S. oryzae were first scanned by X-rays. Thesame infested kernels were then mixed with uninfested bulk grain and scanned using a spectrophotometer.The infestation level in the soft X-ray method was quantified by counting the number of infested and unifestedkernels whereas in the NIR spectroscopy method it was quantified by the mass of infested and unifested grain.The identification of infestations by both methods increased with the increase in the developmental stage of theinsect from larvae to adult stage. The soft X-ray method has the advantage of potential application in graininspection over NIR spectroscopy where the number of infested or insect-damaged kernels is an essentialinformation. The NIR spectroscopy analyzing bulk samples has applications in grain management such asfumigation where the identification of insect species is critical and precise quantification of infestations is notvital.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".