In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra
Bibliographic record
Abstract
Maqbool, R., Percival, D. C., Adl, M. S., Zaman, Q. U. and Buszard, D. 2012. In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra. Can. J. Plant Sci. 92: 1155–1161. Remote sensing techniques have the potential to serve as an important nutrient management tool in wild blueberry. The potential of visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) spectroscopy was evaluated during 2006 (sprout/vegetative phase of production) to estimate foliar nitrogen (N). Canopy reflectance measurements were taken from two nutrient management experimental sites located in Nova Scotia (NS) and New Brunswick (NB). Partial least squares regression (PLSR) estimated foliar N, giving the coefficients of determination (R2) values ranging from 0.69 to 0.85, and root mean square errors of cross validation (RMSECV) from 0.16% (±8.29% of mean) to 0.24% (±12.43% of mean) for different spectral ranges used in this study. The green peak region located in the VIS region best estimated foliar N. The tested spectral ranges differed in their predictive ability, but generally followed the biochemical basis. Variable importance in projection scores (VIP), regression vector coefficients and PLSR loading weights (LWs) plots highlight the importance of wavebands (∼550 nm, ∼610 nm, 1510 nm, ∼1690 nm, ∼1730 nm, ∼1980 nm and ∼2030 nm) for in situ foliar N estimations. Thus, it was concluded that reflectance spectra may be used to estimate and ultimately map foliar N in wild blueberry production. The results illustrated the ability of multivariate techniques, such as PLSR to explore hyperspectral data and estimate leaf tissue nutrient content.
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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".