Proximal Sensing and Relationships to Soil and Vine Water Status, Yield, and Berry Composition in Ontario Vineyards
Bibliographic record
Abstract
Proximal sensing technology was developed to overcome many of the restrictions related to satellite- or aircraft-based remote sensing systems. Ground-based proximal sensing systems collect multispectral images in the visible and near-infrared wavebands and calculate vegetation indices, such as the normalized difference vegetation index (NDVI). The objective of this study was to assess the usefulness in viticulture of NDVI measurements acquired by the GreenSeeker™ optical sensor technology and to relate those measurements with grapevine physiological indicators. It was hypothesized that variability in vegetative expression, yield, and plant water status would relate to NDVIs and that differences in grape composition, including phenols and color, would be identified. It was also hypothesized that spatial variability in the study plots would exhibit temporally stable patterns. Results suggested that NDVI successfully established relationships with most variables; positive relationships were exhibited with vine size and yield components, while inverse correlations were demonstrated with phenols in red cultivars and monoterpenes in Riesling. Clustering patterns in NDVI were confirmed by <i>k</i>-means clustering analysis and Moran's <i>I</i> spatial autocorrelation index. The usefulness of the GreenSeeker proximal sensing tool was confirmed and is indicative of the future applicability of this technology to divide vineyards into subblocks of different productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".