Quantification of browning in apples using colour and textural features by image analysis
Bibliographic record
Abstract
This study analyses the effect of browning through image analysis based on colour and textural features in fresh-cut apple slices. A computer vision system (CVS) was developed for image acquisition, which consisted of a digital camera and a florescent lamp source for illumination with a contrasting background. The CVS was calibrated using standard colour values and a model was developed by artificial neural network technique. Three varieties of apples such as Honey crisp, Granny Smith, and Golden Delicious were used for the analysis. The apples were freshly cut and subjected to image acquisition. Normalized colour features (L*, browning index, hue, and colour change) and textural features (entropy, contrast, and homogeneity) were analysed from the acquired images. The varieties Honey Crisp and Granny Smith did undergo browning within 120 min, whereas Golden delicious did not brown significantly. The study concluded that colour and textural features were important decision features for detecting browning in apples through image analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".