UV-spectrum remote UVA imaging for use in precision agriculture
Bibliographic record
Abstract
Floral UV-reflectance is considered an essential factor in plant-pollinator interactions. UVreflective pigments on reproductive structures allow pollinators to locate flowers from the air and differentiate conspecifics. This relationship is possible due to the ability of pollinators, such as bees, to see in the UV spectrum (300-400nm). Despite this well-documented visual signalling strategy of flowering plants, our literary review indicated that few crop species have had their UV spectral reflectance documented. Without considering UV reflectance, breeding efforts could render flowers cryptic to pollinators, decreasing yield and harvest quality. Strawberry cultivars were spectrally analyzed and compared to their wild counterpart. White-flowering cultivars showed higher pollinator visibility, whereas the red-flowering cultivar was cryptic. Bee vision (300-650nm) is adapted to detect flowers. This project mimicked the bee vision range in designing and creating the Nature Inspired Detector (NID) to detect strawberry flowers remotely. Two state-of-the-art AI algorithms were trained on a custom strawberry flower image dataset where YOLOv5 outperformed Faster R-CNN( mAP 0.978 vs. 0.912, respectfully). The NID was then field deployed on a UAV over a strawberry field. Results were comparable to a contemporary study, but the NID had a faster training time (0.3 vs. 5.5 hrs) and higher mAP (0.951 vs. 0.772).
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How this classification was reachedexpand
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".