Effects of Chemical Fertilizer Reduction on Leaf Spectral Characteristics of Greenhouse Long Pepper
Bibliographic record
Abstract
The sensitive period of leaf spectral index was studied by analyzing the leaf spectral reflectance in the response to nitrogen, phosphorus and potassium fertilizer at different growing stages of greenhouse Long pepper under the condition of fertilizer application reduction, to provide a non-invasive, simple and rapid nutrition diagnosis of nitrogen, phosphorus and potassium.The leaf spectral reflectance was measured using Unispec-SC spectrometer at initial flowering, initial fruiting, green ripening, color changing, red ripening stage of pepper leaves under the condition of different nitrogen, phosphorus and potassium fertilizer levels.The variation of leaf spectral reflectance in developmental phase was the least in visible spectrum band.In the near infrared region, the leaves of pepper showed lower spectral reflectance and higher chlorophyll content in different fruit growth stages, optimized fertilization treatment with a reduction of 30% and optimized fertilization treatment with a reduction of 45%.Under different nitrogen, phosphorus and potassium fertilization levels, the sensitive bands of nitrogen, phosphorus and potassium in leaves at different fruit development stages were different.Under different fertilizer treatment conditions, nitrogen, phosphorus and potassium of leaves had different light sensitive bands at different developmental stages.The sensitivity differences of leaf spectral reflectance to N, P and K in different growth stages of greenhouse Long pepper were analyzed, and the sensitive periods of N, P and K were explored by using leaf spectral index, aiming to provide the best time window for rapid, accurate and non-destructive nutritional diagnosis of greenhouse Long pepper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".