The Feasibility, Practicality and Uses of Detecting Crop Water Stress in Southern Ontario Apple Orchards with a UAS
Bibliographic record
Abstract
UAS (Unmanned Aerial Systems) are becoming more common place in agricultural sites around the world. While the accuracy of achieving NDVI (Normalized Difference Vegetation Index) from a UAS is well understood, few studies have attempted to acquire other plant health attributes such as CWSI (Crop Water Stress Index), particularly in horticulture such as apple orchards. In addition, no academic studies up to the time of this writing have explored the perceived usefulness of data obtained from a UAS for the average farmer. This study explored the practicality and feasibility of using UAS for apple orchards in Southern Ontario. This study sought to find out if NDVI and CWSI can be accurately obtained from a UAS for apple orchards and if this data can be feasibly obtained and is practical for the average Ontario apple farmer. By flying a UAS over a volunteer orchard and conducting charrette style interviews with orchard owners with the obtained data, the results showed that data is indeed useful to the farmers, despite improvements needed for CWSI accuracy. However, this data is only useful during key times of the growing season and obtaining this data, while feasible, requires planning and logistics around weather and government red tape. This study has laid the ground work for future studies to use as a staging point to improve CWSI estimate accuracy, create new methods of observing health attributes or diseases in apple orchards, and obtain more information on the usefulness of UAS data for Ontario farmers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".