Improved Leaf Area Index Retrieval Using 3-D Point Clouds From UAV Imagery
Bibliographic record
Abstract
Leaf area index (LAI) serves as a key ecophysiological parameter for assessing plant growth and is particularly vital for crop monitoring. Using unmanned aerial vehicle (UAV)-based point cloud data generated through photogrammetry techniques offers valuable structural insights into crops, facilitating LAI retrieval. This study introduces a method for estimating LAI from 3-D point clouds. By employing spherical voxel partitioning, the vegetation gap fraction is computed based on the spatial distribution of point clouds. Furthermore, the leaf inclination angle is determined through triangular patch collections reconstructed from 3-D point clouds. Projection functions, accounting for varying zenith perspectives, are developed considering the leaf inclination angle. Subsequently, the combination of vegetation gap fraction and projection functions is used within the Beer–Lambert law framework to calculate LAI. Validation against ground measurements demonstrates a strong correlation between measured and retrieved LAI ($R^{2} = 0.64$, RMSE = 0.43), affirming the effectiveness of the proposed method in estimating LAI using UAV-based structure from motion (SfM) point cloud data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".