Rice Foreign Object Classification Based on Integrated Color and Textural Feature Using Machine Learning
Bibliographic record
Abstract
A blend of natural and artificial foreign objects can be used to determine the rice quality.The agricultural industry, particularly rice plants, has demonstrated great success rates for object detection based on image processing.Most food quality studies can be seen from the image shape color and size, and the rice quality can be seen from the absence of the foreign object.HSV color and GLCM texture are used to classify natural and non-natural foreign object images using the support vector machine (SVM) algorithm and other comparison methods, namely decision tree and naive Bayes.The dataset for foreign objects consists of 80 images, 20 of which are each of the following classes: stone, grain, yellow-broken, and red-black.The dataset will be preprocessed to obtain the feature values for color and texture.SVM method with cross-validation, the highest accuracy value is 96.83%, a decision tree is 87.31%, and naive Bayes is 82.54% in detecting natural and non-natural foreign objects.The use of cross-validation techniques with a value of K=5 gives an average accuracy increase of 10% compared to those without cross-validation.These results show that natural foreign objects in different classes can be appropriately detected using a combination of color and texture features in the SVM classification method and cross-validation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".