Digital Phenotyping of Drought Tolerance in Brassica carinata
Bibliographic record
Abstract
Drought is becoming a major environmental challenge in the Canadian prairies. The oilseed Brassica carinata, a dedicated industrial feedstock crop used for biofuel production, is to be climate resilient. However, no systematic studies have been conducted to investigate drought tolerance in this crop. To address this knowledge gap, this study aims to identify digital phenotypes associated with drought tolerance. We grew 47 B. carinata Nested Association Mapping (NAM) founder lines and two B. napus checks under both irrigated and rainfed conditions at the AAFC Saskatoon Research Farm in 2023, a year with precipitation well below normal. Phenological and agro-morphological traits were recorded and a UAV equipped with RBG, multispectral and thermal sensors provided digital phenotyping data at key growth stages from rosette to maturity. We identified a stress susceptibility index that is strongly correlated (R2=0.9) with seed yield reduction percentage in rainfed compared to irrigated conditions and thus indicative for drought tolerance. Further, Lasso regression model was used to develop a descriptive model for drought tolerance. The model showed that the most important predictors for drought tolerance under rainfed conditions were plant height, harvest index, normalized difference yellowness index (NDYI) (61 DAS), crop volume (81 DAS), normalized difference water index (NDWI) (102 DAS), photochemical reflectance index (PRI) (102 DAS), canopy temperature at 58 DAS, and canopy temperature at 74 DAS. Among the UAV-derived traits, canopy temperature at 58 DAS, NDYI (61 DAS) and crop volume (81 DAS) emerged as the most critical contributors, indicating that they may serve as proxies for predicting drought tolerance. These findings highlight the utility of integrating UAV-based thermal and multispectral data with quantitative agronomic measures to assess drought tolerance in Brassica species.
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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 | 0.001 |
| 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) | 0.011 | 0.004 |
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".