Quantifying the Diversity of Agronomic Traits of Cicer Milkvetch (Astragalus cicer L.) Germplasm Collections Using UAV-Based Imagery
Bibliographic record
Abstract
Cicer milkvetch (Astragalus cicer L.) is a non-bloat perennial forage legume well suited for stockpile grazing because of its persistence, high late season quality and yield in the moist areas of the Canadian Prairies. Remote sensing via unmanned aerial vehicles (UAV) has the potential to accurately phenotype forage germplasm with less manual labour. The objectives of this study were: 1) Evaluate the diversity and relationship among 27 populations of cicer milkvetch using agro-morphological traits, 2) Differentiate a sub-set of cicer milkvetch populations from June to mid-October using UAV-based multispectral imaging, and 3) Identify UAV-based traits with significant correlations to the forage DMY and growth of cicer milkvetch. Near Clavet Saskatchewan, a completely randomized nursery of 27 cicer milkvetch populations was characterized nine times from June to mid-October in 2020 and 2021. Measurements included the agro-morphological traits maximum stem length, leaf number per stem, stem density, rhizome spread and plant area scores, and the UAV-based traits NDVI green area and NDVI canopy volume. The first forage harvest occurred in late June and the stockpile harvest in mid-October. Excluding leaf number per stem (first harvest) and plant area (stockpile harvest), significant differences (p<0.05) were identified between populations at both harvest times for the agro-morphological traits. Based on the agro-morphological traits at both harvest times, the first three principal components described 89% of the variation in the data. NDVI green area and NDVI canopy volume were able to differentiate (p<0.05) high and low vigour populations across the growing season. Among the agro-morphological traits, maximum stem length had the highest correlation with forage DMY at the first (r 0.74) and stockpile (0.83) harvests. NDVI green area had the highest correlation with forage DMY among the UAV-based traits at the first harvest (r 0.91) and the correlation with stockpiled forage DMY was improved when NDVI green area was recorded in mid-September (r 0.92). Across the growing season, NDVI green area accurately modelled changes in maximum stem length (r2 0.42-0.60). With a more user-friendly image analysis process, UAV measurements could be an accurate tool for the precision phenotyping of forage germplasm.
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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".