X‐ray Image Analysis to Detect Infestations Caused by Insects in Grain
Bibliographic record
Abstract
ABSTRACT Insect infestations in stored wheat affect the chemical characteristics and baking qualities of wheat flour, and insect‐infested flours are unacceptable in the baking industry. The efficiency of the soft X‐ray method to detect infestations caused by Cryptolestes ferrugineus (Stephens), Tribolium castaneum (Herbst), Plodia interpunctella (Hübner), Sitophilus oryzae (L.), and Rhyzopertha dominica (F.) in wheat kernels was determined in this study. Wheat kernels infested by different insects were prepared by artificial implantation of insect eggs or by introducing adult insects in wheat samples. Kernels infested by different stages of the insects were X‐rayed until the adults emerged from the kernels. A total of 57 features using histogram groups, histogram and shape moments, and textural features were extracted from the X‐ray images and a linear‐function parametric classifier was used to identify the insect‐infested kernels. The parametric classifier identified more than 84% of infestations due to C. ferrugineus and T. castaneum larvae. The infestations by C. ferrugineus pupae‐adults and P. interpunctella larvae were identified with >96% accuracy. Kernels infested by different stages of S. oryzae and R. dominica larvae were identified with >98% accuracy. Using the Berlese funnel method, 67, 51, and 81% of first, second, and third instars of C. ferrugineus, respectively, were extracted in 6 hr. The same infested kernels were all categorized as infested by the parametric classifier. When kernels infested by different insects were pooled together, the parametric classifier correctly identified 74% of uninfested and 94% of infested kernels by the internal and external grain feeders. The 26% false positives identified from the independent test was caused by one sample infested by T. castaneum. When that sample was removed from the training set, the false positives were reduced to 16%, and 92.7% of infested kernels by different insects were correctly identified.
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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 | 0.001 |
| 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) | 0.002 | 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".