UV reflectance in crop remote sensing: Assessing the current state of knowledge and extending research with strawberry cultivars
Bibliographic record
Abstract
Remote sensing of spectral reflectance is a crucial parameter in precision agriculture. In particular, the visual color produced from reflected light can be used to determine plant health (VIS-IR) or attract pollinators (Near-UV). However, the UV spectral reflectance studies largely focus on non-crop plants, even though they provide essential information for plant-pollinator interactions. This literature review presents an overview of UV-reflectance in crops, identifies gaps in the literature, and contributes new data based on strawberry cultivars. The study found that most crop spectral reflectance studies relied on lab-based methodologies and examined a wide spectral range (Near UV to IR). Moreover, the plant family distribution largely mirrored global food market trends. Through a spectral comparison of white flowering strawberry cultivars, this study discovered visual differences for pollinators in the Near UV and Blue ranges. The variation in pollinator visibility within strawberry cultivars underscores the importance of considering UV spectral reflectance when developing new crop breeding lines and managing pollinator preferences in agricultural fields.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".