A CNN-Based System for Classification and Real-Time Detection of Raisin
Bibliographic record
Abstract
Although Turkey ranks first in the world in terms of raisin exports, it ranks fourth in revenue.The main reason for this problem is that the quality of exported raisins does not meet the desired level.In this study, an intelligent real-time system was developed to determine suitability for export by classifying raisins according to color and capstem amount according to the criteria of TS 3411.A dataset of 2336 Sultana raisins was created and expanded to 10,544 images using data augmentation techniques such as exposure adjustment, rotation, and noise addition.These techniques not only increased the dataset size but also significantly improved the model performance, as evidenced by the remarkable success of the CNN models with an average mAP of 98.8%.The images were evaluated in real time in the developed GUI software and their compliance with the standard was determined.The study is the first of its kind to classify and detect raisins according to the standard TS 3411 using an intelligent real-time system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".