Aerial Insights: Advancing Nitrogen Estimation in Field Crops using Multispectral Imaging
Bibliographic record
Abstract
Abstract. Estimating the nitrogen (N) content of crops is crucial for determining key indicators such as nitrogen use efficiency (NUE). Traditionally, most methods for assessing N content have been destructive, time-consuming, and labor-intensive. In this study, we present a non-destructive approach using unmanned aerial vehicle (UAV) multispectral imagery to estimate crop nitrogen content at various growth stages. Multispectral drone data were collected over canola and wheat fields at three growth stages across two experimental sites in Alberta, Canada, over two growing seasons (2023–2024). Simultaneously, leaf tissue samples were gathered from different nitrogen treatment levels, each replicated four times. Multiple machine learning (ML) models were developed and tested to predict plant nitrogen uptake. Our findings indicate that multispectral imagery can estimate N content in canola with a root mean square error (RMSE) ranging from 0.38 to 0.71 and a coefficient of determination (R2) between 0.77 and 0.92. For wheat, the RMSE values ranged from 0.33 to 0.68, with R2 values between 0.5 and 0.89. The models showed good transferability across both study sites and two years, suggesting the feasibility of scaling N-content estimation to broader areas. Overall, our results highlight the strong potential of UAV-based multispectral imaging as a reliable, non-invasive tool for estimating nitrogen-related parameters, including plant N-uptake and NUE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".