In-season estimation of grain sorghum yield potential using a hand-held optical sensor
Bibliographic record
Abstract
Sensor based nitrogen (N) management technology has helped to improve fertilizer recommendations for various crops. The objective of this study was to estimate the in-season yield potential (YP0) of grain sorghum (Sorghum bicolor L. Moench) using a hand held optical sensor. This experiment was conducted with four levels of N (50, 100, 150 and 200 kg ha−1) and three application timing (Preplant, topdress and split) arranged in a randomized complete block design with three replications at three locations, in Oklahoma in 2004 and 2005. Sensor readings were taken using red (650 ± 10 nm) and green (550 ± 12.5 nm) sensors at sorghum growth stages 2, 3, 5, 6 and 7. Results from statistical analysis have shown that 75 and 77% of the variation in sorghum grain yield was explained by red and green Normalized Difference Vegetation Index (NDVI), respectively at growth stage 3. Similarly, grain N content was correlated to both green and red (coefficient of determination, r2 = 0.61) NDVI readings at growth stage 3. In-season estimated yield (INSEY) derived from green NDVI was also found correlated with final grain yield (r2 = 0.71). The results of this experiment suggest that INSEY can be used as a tool to predict mid-season sorghum grain yield potential at sorghum growth stage 3.
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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) | 0.001 | 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".