THE COMMERCIALIZATION OF REMOTE SENSING AND GIS FOR VINEYARD MANAGEMENT: A SIMPLE BUT POWERFUL APPLICATION OF CHANGE DETECTION
Bibliographic record
Abstract
Remote sensing and GIS have been used for day-to-day vineyard management in a quasi-to-fully operational fashion in the Napa Valley area of California (Greater Napa, Sonoma, Lake and Mendocino Counties) and several other regions of the world for the past five to ten years. This paper reviews some of the key papers in the literature and describes the way in which the tools have been used in a fully operational environment. The focus is on their use as a special but simple case of change detection over time – both year to year and over a season. These applications have ranged from thermal sensing to identify areas prone to frost damage in the Niagara Region of Ontario, to the use of high resolution airborne imagery for viticulture research and management in the in the Napa Valley and Oregon wine regions of the United States and the Niagara Region of Canada. Monitoring changes over time has proven to be one of the most valuable contributions of remote sensing for vineyard management. Given the financially compelling reasons for using remote sensing in vineyard management in many regions of the world, the challenge in commercializing such services in other regions has been surprising.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".