Assisted Canopy Sampling Using Unmanned Aerial Vehicles (UAVs)
Bibliographic record
Abstract
Recently, Unmanned Aerial Vehicles (UAVs) carrying canopy sampling tools have been demonstrated. They present serious advantages in terms of safety and reach, but their time efficiency and ease of use could still be improved. Three main challenges were identified when using such tools: the selection of accessible branches, the lack of precision caused by the pendulum movement of the sampling tool, and the necessity to have two operators. In this paper, we address those issues by presenting the foundation of an architecture to provide sampling assistance on deciduous trees that makes UAV sampling more efficient and easy to use. The UAV is equipped with a DeLeaves canopy sampling tool, an RGB-D camera, and an onboard computer. Computer vision was used to detect and localize branches. Sampling assistance was used to limit the tool's oscillation and reduce the pilot's workload, allowing him or her to control both the UAV and the tool. This assisted canopy sampling solution allowed a sampling phase time of 34 seconds on average on mature deciduous trees, thus reducing the time required by 7 times.
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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 | 0.000 |
| 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) | 0.001 | 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".