Measuring canopy height in soybean and wheat using a low‐cost depth camera
Bibliographic record
Abstract
Abstract Canopy height is an essential trait in high throughput phenotyping that is often only captured as a single point, which is not always representative of canopy height. New 3D depth cameras such as the RealSense D415 (Intel Corporation, Santa Clara, CA, USA) may provide a fast and affordable solution for measuring height from portable, ground‐based phenomics systems. Our goal was to determine if the D415 was effective at measuring crop heights under field conditions in wheat ( Triticum aestivum ) and soybean ( Glycine max ) plots. The D415 camera was integrated into our PlotCam platform using the open software development kit from Intel. Distance arrays were captured for each plot at weekly intervals over the growing season. These were compared to canopy heights measured using a single point LiDAR (SPL) system operated by hand. Over the growing season the D415 heights were significantly correlated with the SPL heights in both wheat and soybean with coefficients of 0.77 and 0.95 and NRMSE 0.23 and 0.17 m, respectively. Early season D415 height measurements were not as similar to the SPL as the mid‐and late‐season measurements in wheat and soybean. The relatively low cost and open software development kit of the D415 makes it a promising tool for high throughput phenotyping applications.
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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".