Multispectral Drone Imaging for Non-Destructive Estimation of Nitrogen Content in Canola and Wheat
Bibliographic record
Abstract
Estimating nitrogen (N) content of crops is essential for obtaining critical parameters such as nitrogen use efficiency (NUE). However, most of the methods developed for N content estimation are destructive and are time- and labor-intensive. Here, we show the results of a non-invasive method developed based on unmanned aerial vehicle (UAV) multispectral imaging of crops to estimate N content at different growth stages. To do so, multispectral drone images of canola and wheat were collected at three different stages of crop growth in an experimental field trial at AAFC Lethbridge, AB, Canada. Leaf tissue samples were also collected concurrently for seven different treatments of nitrogen applications, each of which was replicated four times. Several machine learning (ML) models were trained and evaluated for estimating plant N-uptake. The results show that multispectral imaging can estimate N content in canola with an RMSE of 0.38-0.59 and R2 of 0.77-0.92, while these numbers are 0.33-0.52 and 0.71-0.89 for wheat. We also show that N-content estimations based on multispectral imagery significantly benefit from incorporating ancillary data, such as treatments and image acquisition date into ML models, reducing RMSE by 5-10%. These results show the potential of UAV-based multispectral imaging in acquiring nitrogen-related parameters such as plant N-uptake and NUE measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".