The DeLeaves: a UAV device for efficient tree canopy sampling
Bibliographic record
Abstract
Tree canopy sampling is critical in many forestry-related applications, including ecophysiology, foliar nutrient diagnostics, remote sensing model development, genetic analysis, and biodiversity monitoring and conservation. Many of these applications require foliage samples that have been exposed to full sunlight. Unfortunately, current sampling techniques are severely limited in cases where site topography (e.g., rivers, cliffs, canyons) or tree height (i.e., branches located above 10 m) make it time-consuming, expensive, and possibly hazardous to collect samples. This paper reviews the recent developments related to unmanned aerial vehicle (UAV) based tree sampling and presents the DeLeaves tool, a new device that can be installed under a small UAV to efficiently sample small branches in the uppermost canopy (i.e., <25 mm stem diameter, <500 g total weight, any orientation). Four different sampling campaigns using the DeLeaves tool are presented to illustrate its real-life use in various environments. So far, the DeLeaves tool has been able to collect more than 250 samples from over 20 different species with an average sampling time of 6 min. These results demonstrate the potential of UAV-based tree sampling to greatly enhance key tasks in forestry, botany, and ecology.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".