Jasmine Flower Color Degradation User-Coded Computer Vision Image Analysis Tool and Kinetics Modeling
Bibliographic record
Abstract
Jasmine (Jasminum sambac (L.) Ait.) flowers, valued for their fragrance and essential oils, are extensively used in the flavor, cosmetics, and pharmaceutical industries. However, their useful life is short due to rapid color degradation and browning caused by photo-oxidative stress induced by environmental factors like light, temperature, and humidity. Therefore, the significant reduction in the visual appeal, quality, and economic value necessitates the measurement of temporal color degradation to evaluate the shelf life for jasmine flowers. A developed open-source ImageJ plugin program quantified the color degradation of jasmine petals and pedicles over 25 h. Petal area (>19 mm2) cutoff separated the pedicles. Color degradation kinetics models, including zeroth-order, first-order, exponential decay, Page, and Peleg, using several color indices, were developed, and their performances were evaluated. VEG, hue, chroma, COM, and CIVE color indices were found suitable for kinetics modeling. Peleg and Page models (R2≥0.99) are suitable for petals and pedicles, respectively. Jasmine petals retained their color integrity for longer periods than pedicles. This study underscores the potential of computer vision analysis and kinetic modeling for evaluating flower quality after harvest. The color degradation dynamics were accurately characterized by the kinetic models, which provide actionable insights for optimizing storage and handling practices.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".