Early detection of canopy nitrogen deficiency in winter wheat ( <i>Triticum aestivum</i> L.) based on hyperspectral measurement of canopy chlorophyll status
Bibliographic record
Abstract
Abstract A spectroscopic method was developed to measure the nitrogen status of winter wheat ( Triticum aestivum L.) canopies. Two years of field experiments, including a range of cultivars grown with differing levels of nitrogen fertilization, were conducted and ground‐based hyperspectral data were collected to develop and validate an empirical model for early detection of low canopy chlorophyll content. Canopy reflectance was measured with a spectrometer, fitted with a 25° field of view fibre‐optic adaptor. Canopy chlorophyll density (CCD), representing the total amount of chlorophyll present in the canopy per unit ground area, was combined according to the contribution of winter wheat leaves in different layers of the canopy and related to canopy reflectance. Combined canopy chlorophyll density (CCCD) calculated with both layers 1 and 2 and with layers 1, 2 and 3 were better related to difference vegetation index (DVI=R NIR −R RED , where R NIR and R RED were reflectance at 890 nm and 670 nm, respectively) than CCD in any individual layer. Statistical prediction models of canopy chlorophyll status in winter wheat were developed. The CCCD 1+2 model demonstrated lower root mean square errors and higher modelling efficiencies than those of the CCCD 1 and CCCD 1+2+3 models. Chlorophyll status in the two uppermost layers of the wheat canopy could be quantified using DVI. Therefore, early detection of canopy nitrogen deficiency in winter wheat was achieved.
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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".