Identification of pests hidden in wheat kernels based on support vector machine classifier
Bibliographic record
Abstract
The identification of pests hidden in stored wheats, essential to grain storage safety, is a key difficulty in the research of target detection.This paper introduces the support vector machine (SVM) classifier to identify the pests hidden in wheat kernels, and selects the proper kernel function and parameters to classify various samples.It is verified that the proposed method could accurately detect the pests in wheat kernels.This research provides new insights into the application of pattern recognition in bio-photon detection of pests in stored grains.RÉSUMÉ.L'identification des parasites caché s dans les blé s stocké s, essentielle à la sé curité du stockage du grain, est une difficulté majeure dans la recherche sur la dé tection des cibles.Cet article pré sente le classifieur de machine à vecteurs de support (en anglais support vector machine, SVM) pour identifier les parasites caché s dans les noyaux de blé et sé lectionne la fonction et les paramè tres du noyau approprié s pour classifier divers é chantillons.Il est vé rifié que la mé thode proposé e pourrait dé tecter avec pré cision les parasites dans les noyaux de blé .Cette recherche fournit de nouvelles perspectives sur l'application de la reconnaissance de formes à la dé tection par bio-photon des parasites cacshé s dans les grains stocké s.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".