Novel Curve Fitting Analysis of NDVI Data to Describe Turf Fertilizer Response
Bibliographic record
Abstract
Evaluating the effectiveness of fertilizers on crops without traditional yield is difficult, as clipping collection is time-consuming and not indicative of the desired response to the fertilizer. Remote sensing techniques, like the normalized difference vegetation index (NDVI), have emerged as an effective tool to combat these issues. Canopy reflectance, measured by NDVI, is commonly used to differentiate turfgrass response to nitrogen (N) fertilization treatments; however, advancements in data processing are needed for greater differentiation between treatments and better testing of the effects of fertilizer responses. Presented here is an advancement in the processing of NDVI data by applying a compound exponential (pulse peak) function to changes in NDVI over time, relative to a control. This method’s effectiveness in differentiating between fertilizers was evaluated by applying polymer-coated urea (PCU), an enhanced efficiency fertilizer (EEF), and urea on turfgrass to show the usefulness of the novel curve fitting in highlighting differences in fertilizer response. The field study was carried out on an established Kentucky bluegrass (Poa pratensis L.) blend on a sandy loam soil rootzone maintained with a typical home lawn maintenance regime. Ratios of PCU to urea ranging from 100% urea to 100% PCU with increments of 20% of PCU were used to observe a continuum of response between the two fertilizer types. The N rate for all blends was 73 kg ha−1, with unfertilized plots used as a control to set baseline measurements for the curve fitting. Turfgrass response to N fertilization treatments was assessed through visual turfgrass quality ratings, dry matter yield, and canopy reflectance, where higher urea content resulted in the shortest time to maximum ∆NDVI (24 days), while 100% EEF reached maximum ∆NDVI at 56 days. The novel curve fitting analysis of the NDVI data allowed for differentiation between treatments, provided a measure of fertilizer effects, and differentiated between urea and enhanced efficiency fertilizers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".