Automated precise seeding with drones and artificial intelligence: a workflow
Bibliographic record
Abstract
Aerial seeding with drones has great potential in forest restoration but faces enormous challenges to be efficient and scalable. Current protocols use blanket seeding throughout the area to be restored, meaning a high demand for seed since many seeds arrive in sites unsuitable for establishment. High precision seeding directed to safe microsites at submeter scale could reduce seed use per hectare, reducing economic and ecological costs, while increasing establishment success. Here, we propose an alternative, precision approach to make drone seeding more successful and efficient. This requires (1) submeter‐scale selection of target microsites for seeding founded in ecological knowledge; (2) high‐resolution remote sensing imagery to train artificial intelligence (AI) systems in target microsite recognition; and (3) process automation by transferring target microsite coordinates from the AI system to the drone. This will reduce seed inputs per unit area, seedling establishment failure risks, and drone operation costs.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".