Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning
Bibliographic record
Abstract
This research aimed to develop computer vision and machine learning models to improve durum wheat quality control in Algeria.Durum wheat is a vital cereal crop in Algeria used for many staple foods.Currently, quality control relies on manual evaluation which is too lengthy and tedious.To address this, models utilizing image processing and 200 image descriptors, including 102 texture features, 8 morphological features, and 90 colour features, were developed to automate classification of durum wheat species, varieties, and impurities.An optimized Support Vector Machine (SVM) model was implemented hyperparameters tuning.The models achieved exceptional performance, classifying durum wheat species with 99% accuracy, varieties with 95% accuracy, and impurities with 94% accuracy.This illustrates the significant potential of tailored computer vision and machine learning techniques to enable automated quality control.The models could be integrated into crop certification workflows, increasing productivity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".