The Height Pole: Measuring plot height using a single‐point LiDAR sensor
Bibliographic record
Abstract
Abstract Plant canopy height is an essential trait for phenomics and plant breeding. Despite its importance, height is still largely measured by manual means with a ruler and notepad. Here, we present the Height Pole, a novel single‐point LiDAR (SPL)‐based instrument to measure and record plant and canopy height in the field quickly, reliably, and accurately. An SPL was mounted on the top of a pole and aimed downwards at an adjustable paddle that was positioned at the desired height. A custom app, written for Android OS, saved the plant height data from the SPL to a tablet. The Height Pole was tested against a ruler in the lab, in a field trial setting, and by multiple operators. Indoor and outdoor testing found no significant differences between a ruler and the Height Pole measurements. A test with five operators revealed that measuring, recording, transcribing, and digitizing were on average 20 s per plot slower with a ruler than with the Height Pole. The Height Pole required only one operator to measure and record data, reduced operator fatigue, and by directly writing the data to a .CSV file eliminated transcription errors. These improvements make it easier to collect crop height data on large experiments rapidly and accurately with low input costs.
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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 | 0.001 | 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".