Prediction Models of Corn Yield by NDVI in Function of the Spacing Arrangement
Bibliographic record
Abstract
There is a need for the use of tools to estimate productive potential during corn crop development. Thus, the assistence by means of active optical sensors for the generating of vegetation indexes can provide significant information for the knowledge of the behavior and temporal relation of this index with productive parameters of the agricultural crops. It was aimed to evaluate the temporal behavior of NDVI and its relation with yield of corn in order to generate yield prediction models in plant populations (55, 60 and 65 thousand plants ha-1) in spacing of conventional seeding and twin rows. A factorial 2 × 3 was utilized with four replicates, with a total of 24 experimental plots of 10 m2 in randomized blocks, performing reading NDVI at 5 seasons (30, 45, 60, 75 and 90 days after emergence of the plants DAE). The spacing in twin rows at 30 and 90 DAE for populations of 55 and 60 thousand plants ha-1, respectively, allowed to generate models for the prediction of productivity based on corn NDVI, while the population of 65 thousand plants ha-1 at 45 and 60 DAE there was no adjustment by the prediction model of yield by values close to NDVI for different productivities. In the conventional spacing generating models for the prediction of yield was possible in the populations of 55 and 60 thousand plants ha-1 respectively at 30 and 90 DAE.
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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 | 0.001 |
| 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) | 0.001 | 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".