Determination of Soya Plant Population Using NDVI in the Dasht-e-Naz Agri-Industry
Bibliographic record
Abstract
Numerous efforts have been made to develop various indices using remote sensing data such as normalized differencevegetation index (NDVI), and vegetation condition index (VCI) for mapping and monitoring of yield estimating andassessment of vegetation health and productivity. NDVI and other indices that derive from satellite images are valuablesources of information for the estimation and prediction of crop conditions. In the present paper, NDVI data ofDasht-e-Naz in Iran in 2006 have been considered for crop yield assessment and estimating. The results showed thatthere is acceptable relationship between NDVI and Soya plant population. The correlation between NDVI and plantpopulation in high plant population area of field was (R2=0.923) and for low plant population area was (R2=0.249). Thecrop population models were discussed about high and low plant population in the present paper and could improve infuture with the use of long period dataset. Similar model can be developed for different crops of other locations.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".