Evaluating the effect of light exposure and seed coat on lentil cotyledon color by computer vision
Bibliographic record
Abstract
Abstract A computer vision system and color analysis algorithms were employed to study the influence of UVA (ultraviolet‐A) and visible light on the color of lentils with red, green, and yellow cotyledons. Twenty samples of cotyledons from each of the three‐color classes were subjected to six light treatments (ultraviolet, full‐spectrum visible, red, green, blue, and dark control) for 7 days at room temperature. The International Commission on Illumination L*a*b* (CIE L*a*b*) color values of the individual seeds were obtained before and after each treatment using the computer vision and image analysis system. Results of the analysis showed that light exposure had a statistically significant effect on all three cotyledon color classes. The effect size was largest for green lentils, smaller in yellow, and least in red lentils. By having established that light exposure affects the color of lentil cotyledons, it was hypothesized that seed coats may protect cotyledons against the effects of light exposure and that the degree of protection would vary with seed coat color classification. This hypothesis was tested using green‐cotyledon lentil varieties with different seed coat classes. Results confirmed that light‐induced color loss in the cotyledons was significantly influenced by seed‐coat color class. The order of protective effect of lentil seed coat from least to highest was found to be as follows: gray‐zero tannin, green, normal gray, and black. Thus, breeding for seed coat protection may improve the overall quality of green lentils. The results from this study will be informative to breeding programs that focus on enhancing the cotyledon color of lentils, and in making decisions regarding the dehulling of lentils and the design of dehulled lentil materials handling.
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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 | 0.000 | 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) | 0.001 | 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".