Strategies to Make Use of Plant Sensors‐Based Diagnostic Information for Nitrogen Recommendations
Bibliographic record
Abstract
Improvements of nitrogen use efficiency (NUE) may be achieved through the use of sensing tools for N status determination. Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly affected by N availability that are often used as a surrogate to direct plant N status estimation. Approaches with near‐term operational sensors, handheld and tractor‐mounted, for proximal remote measurements are considered in this review. However, the information provided by these tools is unfortunately biased by factors other than N. To overcome this obstacle, normalization procedures such as the well‐fertilized reference plot, the no‐N reference plot, and relative yield are often used. Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen sufficiency index), INSEY (in‐season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor‐determined CI (chlorophyll index) are also reviewed. In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted. Near‐term operational sensing can benefit from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specific conditions (i.e., genotype, growth stage, or crop density). However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".