Use of Nondestructive Sensors to Assess Nitrogen Status in Potted Dianthus (Dianthus chinensis L.) Production
Bibliographic record
Abstract
Optical sensors are a fast and nondestructive new technology used to estimate plant chlorophyll content by measuring leaf reflectance or absorbance of light. The objective of this study was to evaluate the reliability of normalized difference vegetative index (NDVI) values calculated by the GreenSeeker™ hand held sensor as an indirect indicator of dianthus (Dianthus chinensis L.) N status, and investigate nutrient supplementation on deficient ‘Telstar™ White’ and ‘Telstar™ Red Picotee’ plants. Pots were supplemented with 0, 5, 10, 15, and 20 g of 15N-3.9P-10K controlled release fertilizer (CRF). Soil and plant analysis development (SPAD) chlorophyll meter, GreenSeeker™ NDVI sensor, and atLEAF meter readings were recorded for four consecutive weeks. At 49 d after planting (DAP), the 0 and 5 g treatments were found deficient and half of the pots were supplemented with extra fertilizer for treatment correction (C 0 and C 5, respectively). The SPAD and atLEAF readings showed stronger correlation to actual leaf N concentration compared with the GreenSeeker™. The GreenSeeker™ readings were less correlated with leaf N concentration at early stages of establishment due to the plants’ small size and background noise, as well as at later stages of establishment due to anthesis. Dianthus quality responses generally increased as fertilizer rates increased. The additional correction treatment showed a significant improvement in C 5 and no significant improvement on C 0, indicating that the correction treatment was beneficial for dianthus when initial amount of fertilizer was applied.
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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".