Determination of a Multivariate Indicator of Nitrogen Imbalance (MINI) in Potato Using Reflectance and Fluorescence Spectroscopy
Bibliographic record
Abstract
In this study, we evaluated the potential of reflectance and fluorescence for the detection of the Compositional Nutrient Diagnosis (CND) N index (IN). The CND reflects nutrient deficiencies as well as nutrient interactions, contrary to conventional methods of nutrient stress detection. Potato plants (Solanum tuberosum L. cv. Superior) were grown in a greenhouse, and three different nutrient deficiencies (K, Mg, and N) were induced at three levels and compared with a control receiving a complete nutrient solution. Nitrogen deficiency induced a significant biomass reduction compared with control plants whereas no significant effect was observed for K or Mg treatments. Foliar analyses were realized to compute the CND_r2 using the CND. The ANOVA conducted on CND_r2 and IN showed significant differences only between N‐deficient and control plants. Using a canonical discriminant analysis over reflectance and fluorescence indices, it was possible to correctly classify 96.6% of potato plants in its corresponding IN class. A new Multivariate Indicator of Nitrogen Imbalance (MINI) was developed using the canonical variable computed from reflectance and fluorescence indices. The MINI can detect almost 70% of the N‐deficient plants and more than 90% of the N‐sufficient plants. This indicator allows a rapid data acquisition and handling and provides deficiency detection within the time‐window for plant response to recovery fertilization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".