Hybrid Enhanced Featured AlexNet for Milled Rice Grain Identification
Bibliographic record
Abstract
Rice is a widely cultivated grain with numerous genetic variants that can be distinguished by their unique texture, shape, and color characteristics.Accurate classification and evaluation of seed quality depend on the ability to identify these traits.In this study, we propose a novel Hybrid Enhanced Featured AlexNet model for identifying eight varieties of milled rice, including arborio, basmati, ipsala, jasmine, jhili, masoori, HMT, and karacadag.Our approach combines the use of a pre-trained AlexNet model with multilayer feature fusion to extract deep features, which are then supplied to a Support Vector Machine (SVM) for classification.Our proposed model achieves an impressive accuracy of 99.63%, sensitivity of 99.63%, specificity of 99.95%, precision of 99.64%, and an F1 score of 99.63%.Our methodology has significant potential for application in the food processing sector to determine the price of various milled rice varieties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".