Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nitrogen (N) content is an important factor that can affect wheat production. The non-destructive testing of wheat canopy leaf N content through multi-angle hyperspectral remote sensing is of great importance for wheat production and management. Based on a 2-year experiment for winter wheat in Lethbridge (Canada), Zhengzhou (China), and Kaifeng (China) growing under different cultivation practices, the authors studied the relationships between N content and wheat canopy spectral data in solar principal plane (SPP) and perpendicular plane (PP) at different observation angles. Modeling was conducted according to the spectrum index with the highest correlation coefficient and the corresponding observation angle. The results showed that correlation coefficient between the spectral index and canopy leaf N content at each observation angle of the SPP was significantly higher than that of the PP. Significant differences in the correlation coefficient were also observed at different observation angles of the same observation plane, and the correlation coefficients of angles of −30° and −40° were higher than others. A model fitted by a power function by using mND705 as independent variable at an angle of −40° in the SPP showed the highest accuracy.
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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 | 0.001 | 0.001 |
| 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 it