Predicting Nitrogen Use Efficiency of Brassica napus Lines Using RGB and Multi-Spectral High Throughput Phenotyping
Bibliographic record
Abstract
Nitrogen is one of the largest inputs in production of oilseeds as it is often the most limiting nutrient. Understanding how nitrogen is used throughout the growing season can help improve the nitrogen use efficiency (NUE) of the crop. This is an important consideration for both breeders and producers for economic and environmental sustainability. The NUE is defined as the seed yield per unit of available nitrogen. Analyzing whole-plant nitrogen movement throughout the growing season is critical to understanding the requirements and use efficiency of nitrogen within the plant. The purpose of this research is to investigate vegetation indices using canopy spectral reflectance calculated from RGB and multi- spectral images that would correlate to the NUE of 56 B. napus lines in Saskatoon, SK. High-throughput phenotyping (HTP) is a technique that is coupled with remote sensing to create effective methods for rapid n on-destructive and large-scale phenotypic data acquisition. These methods will enable the study of NUE in B. napus that will lead to the development of breeding techniques to improve varieties by taking images frequently throughout the growing season to track nitrogen content and movement. This research will be conducted under two nitrogen treatments, with full plant destructive sampling to be used as ground truth data. Samples from each plot will be analyzed for total nitrogen content using LECO combustion and NIR spectroscopy to correlate with the processed images from the UAV. Early season non-destructive in-situ measurements with a handheld greenseeker and chlorophyll content meter will be used as supportive data to the UAV. The UAV will be equipped with two separate sensors, an RGB and a 5-band multispectral sensor that has the ability to create vegetation indices using the red, blue, green, near-infrared and red-edge bands. This research of optimizing a vegetation index that c an be used in the prediction of NUE calculation throughout the growing season will increase speed and precision in crop research by reducing the need for time consuming labour intensive visual and manual in-field measurements. Link to Video Presentation: https://youtu.be/gYOBYiEuIio
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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 | 0.000 | 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 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".