Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?
Bibliographic record
Abstract
Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a Soil Plant Analysis Development (SPAD) meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (p < 0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".