Potential of a Remotely Piloted Aircraft System with Multispectral and Thermal Sensors to Monitor Vineyard Characteristics for Precision Viticulture
Bibliographic record
Abstract
Grapevines are subjected to many physiological and environmental stresses that influence their vegetative and reproductive growth. Water stress, cold damage, and pathogen attacks are highly relevant stresses in many grape-growing regions. Precision viticulture can be used to determine and manage the spatial variation in grapevine health within a single vineyard block. Newer technologies such as remotely piloted aircraft systems (RPASs) with remote sensing capabilities can enhance the application of precision viticulture. The use of remote sensing for vineyard variation detection has been extensively investigated; however, there is still a dearth of literature regarding its potential for detecting key stresses such as winter hardiness, water status, and virus infection. The main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection. To accomplish the objective, remotely sensed data such as the normalized difference vegetation index (NDVI) and thermal imaging from RPAS flights were measured from six commercial vineyards in Niagara, ON, along with the manual measurement of key viticultural data including vine water stress, cold stress, vine size, and virus titre. This study verified that the NDVI could be a useful metric to detect variation across vineyards for agriculturally important variables including vine size and soil moisture. The red-edge and near-infrared regions of the electromagnetic reflectance spectra could also have a potential application in detecting virus infection in vineyards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".