Use of Remote‐Sensing Imagery to Estimate Corn Grain Yield
Bibliographic record
Abstract
Remote sensing—the process of acquiring information about objects from remote platforms such as ground‐based booms, aircraft, or satellites—is a potentially important source of data for site‐specific crop management, providing both spatial and temporal information. Our objective was to use remotely sensed imagery to compare different vegetation indices as a means of assessing canopy variation and its resultant impact on corn ( Zea mays L.) grain yield. Treatments consisted of five N rates and four hybrids, which were grown under irrigation near Shelton, NE on a Hord silt loam in 1997 and 1998. Imagery data with 0.5‐m spatial resolution were collected from aircraft on several dates during both seasons using a multispectral, four‐band [blue, green, red, and near‐infrared reflectance] digital camera system. Imagery was imported into a geographical information system (GIS) and then georegistered, converted into reflectance, and used to compute three vegetation indices. Grain yield for each plot was determined at maturity. Results showed that green normalized difference vegetation index (GNDVI) values derived from images acquired during midgrain filling were the most highly correlated with grain yield; maximum correlations were 0.7 and 0.92 in 1997 and 1998, respectively. Normalizing GNDVI and grain yield variability within hybrids improved the correlations in both years, but more dramatic increases were observed in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This suggested GNDVI acquired during midgrain filling could be used to produce relative yield maps depicting spatial variability in fields, offering a potentially attractive alternative to use of a combine yield monitor.
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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 | 0.001 | 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 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".