An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data
Bibliographic record
Abstract
Currently, Unmanned Aerial Vehicle (UAV)-based remote sensing is a flexible and reliable approach to gather data for agricultural crop intra-field monitoring. This study proposes real-time and low-cost approaches for crop leaf area index (LAI) estimation using UAV-based 3D point cloud data at field-scale. Crop LAI is an indicator of crop growth variation within crop fields which is one of the most essential crop parameters in crop growth models to predict other crop parameters including chlorophyll, biomass and final yield. After converting a circle with a radius of 2 meters 3D point cloud data to spherical projection, the sampling 3D point cloud data will be converted to a hemispherical photograph. The crop canopy LAI is then calculated from this hemispherical photograph using the gap fraction method. From the experiments over a winter wheat field, the estimated LAI from the UAV-based 3D point cloud data is highly correlated with the LAI estimated from an in-situ fisheye camera, the R2are 0.8995 and 0.8658 for 4 rings and 5 rings view angles calculation, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".