The use of airborne lidar for orchard tree inventory
Bibliographic record
Abstract
The tree inventory in orchards is of great interest for orchard management and for government insurance plans. However, the conventional inventory is time‐consuming and expensive. Here a remote sensing method is introduced for orchard inventory. Airborne LIDAR (light detection and ranging) data were employed to obtain tree topography, and multispectral images were used as a reference. LIDAR vector data were converted to raster data for tree crown delineating purpose and in order to be easily superimposed on multispectral data in the same database. A tree crown delineation model was developed using a tree height image derived from the difference between canopy and ground LIDAR altitudes. The number of trees was computed from the delineation model. Spatially separated trees were precisely counted by fine definition of their crowns. For larger trees, although they have irregular crown form, like multi‐tops, holes in the centre or overlapped branches, the model developed in this study provided reliable results for crown delineation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".