DETECTION AND DISCRIMINATION OF NUTRIENT DEFICIENCIES IN SUNFLOWER BY BLUE-GREEN AND CHLOROPHYLL-<i>A</i>FLUORESCENCE IMAGING
Bibliographic record
Abstract
Fluorescence imaging was utilized to demonstrate the potential of blue-green fluorescence (BGF) and chlorophyll-a fluorescence (ChlF) to discriminate nitrogen (N), phosphorus (P), and potassium (K) deficiencies in sunflower plant showing similar growth inhibition. Only K-deficient leaves displayed significant increase of the BGF intensity. The epidermal UV-transmittance estimated by the ratio of ChlF intensities induced by UV and blue excitations (ChlFUV/ChlFBLUE) markedly decreased in both N- and P-deficient leaves but only in the latter that we observed significant decrease of the ratio of red and far-red ChlF intensities (RF/FRF) (that is inversely related to leaf chlorophyll concentration). The BGF increase in K-deficient was limited at leaf apex and margins and was spatially correlated to localized RF/FRF increases. Images analysis allows a better interpretation of the fluorescence changes by showing the spatial relationships between BGF, the ChlFUV/ChlFBLUE and the RF/FRF ratios that are indicative of physiological disturbances occurring in leaves of nutrient deficient plants.
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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".