Quantification of browning in apples using colour and textural features by image analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it