Field-Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging
Bibliographic record
Abstract
This study explored how to use UAV-based multispectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data were collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices were collected utilizing portable phenotyping instruments, and agronomic data were collected manually. The YOLOv8 detection model was utilized for field-scale wheat plot detection. Four ML algorithms—decision tree (DT), random forest (RF), gradient boosting (GB), and extreme GB (XGBoost were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm's predicting ability across all growth stages, with a root-mean-square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and ML in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".