Precision phenotyping in wheat: LiDAR-based plant height estimation and lodging classification using uncrewed ground vehicles
Bibliographic record
Abstract
Accurate phenotyping of wheat traits is essential for advancing breeding programs and crop science research. This study employs the University of Saskatchewan Field Phenotyping Systems (UFPS), an uncrewed ground vehicle (UGV), across six Canadian research stations during the 2023 and 2024 field seasons to explore LiDAR-based evaluation of plant height and lodging in spring wheat. A total of 90 plots of 30 historical wheat cultivars were planted in a Randomized Complete Block Design (RCBD), with LiDAR data collected at key growth stages. Four methods—height distribution vector, overhead projection, orthographic projection, and voxelized spatial grid—were assessed for height and lodging classification. For plant height, overhead projection (95.98%) and orthographic projection (96.05%) delivered the highest accuracy, with RMSE and MAPE at 3.7 cm and 3.55%. Voxelized spatial grid and height distribution vector methods showed lower performance, with R-squared values of 88.15% and 84.37%. For lodging, overhead projection excelled, achieving up to 0.97 Quadratic Weighted Kappa (QWK), and 98.51% Macro-F1 (MF1), especially in 2- and 3-class scenarios. The voxelized spatial grid performed well, while the height distribution vector and orthographic projection lagged with more classes. These results establish overhead projection as a robust method for high-throughput phenotyping in spring wheat.
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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".