Estimating yield, nitrogen and biomass of brassica naps using high throughput phenotyping methods
Bibliographic record
Abstract
Canola (Brassica napus L.) is an important oilseed crop in Canada, with more than 18 million tonnes of yield in 2022, making Canada the largest global producer (Statistics Canada, 2023; USDA, 2023). In 2013, the Canola Council of Canada proposed the goal of reaching an average of 3400 kg/ha average yield by 2025, an increase from 2280 kg/ha in 2013, through harvest, pest and fertility management, plant establishment and genetic improvement. Developing tools that can be used by breeders to increase the efficiency and speed of developing superior varieties will be of utmost importance towards reaching this target. The prevalence of high-throughput phenotyping methods in literature has increased in recent years with the aim of reducing the need for manual labour by automating routine measurements and the development of predictive models. This in turn, may allow breeders the option of using remote sensing tools to collect data in more environments. This research aims to investigate the use of multi-spectral sensors onboard an unmanned aerial vehicle (UAV) to develop predictive model equations for the biophysical components of B. napus that contribute to the Nitrogen Use Efficiency (NUE). Yield (kg/ha), nitrogen (N) recovery in yield, biomass (g/m2 and per plant) and N content (mg) were regressed with spectral indices and simple ratios calculated from a multispectral sensor, UAV-derived growth measurements, height (m), area (m2) and volume (m3) and manually measured temporal development measurements of days to flower, duration of flower, reproductive duration and days to maturity as predictors. Stepwise regression with backwards selection was used to develop predictive equations where yield was estimated with a coefficient of determination (R2) of 0.74 using 12 independent variables. The N concentration in seed had a moderate estimation (R2=0.67), as well as total N content (kg/ha) in yield (R2=0.6) and biomass (g/m2) at maturity (R2=0.66). The total plant N content (mg) was regressed with the predictors at flowering, pod-fill and maturity. Single plant samples had a high coefficient of determination (R2=0.75-0.95) at flowering, (R2=0.77-0.92) at pod-fill and (R2=0.83) at maturity. The N concentration (%) in plant tissue presented weak coefficients of determination. Thus, this thesis successfully developed a realistic and practical predictive technique to identify the in-field variability of NUE related parameters using UAV’s that can be implemented within a breeding program.
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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 | 0.000 | 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".