Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis
Bibliographic record
Abstract
The objectives of this work wereto investigate the relationships between chlorophyll a+b concentration in leaves (Cab) and grape composition parameters in vineyards affected by iron chlorosis, andstudy whether the assessment of Cab from hyperspectral remote sensing imagery could be useful to map different potential quality zones in these vineyards.A field trial was conducted in a vineyard with the chlorosis susceptible cultivar. 'Tempranillo', over '110 Richter', located in Northern Spain. Three experimental treatments were applied: 0, 2, and 4 foliar sprayings with a ligninsulphonate derived product (10 % water soluble Fe) in a randomized design with 3 replications. The yield and grape composition parameters at harvest were measured for each base-plot (10x10 m in size), and related with chlorophyll concentration in leaves. On the other hand, a total of 24 'Tempranillo' commercial vineyards were identified for field and airborne data collection with CASI hyperspectral sensor, comprising 103 study areas of 10x10 m in size. A total of 1467 leaves were collected for determining pigment concentration and optical properties. Several narrow-band vegetation indices were calculated from leaf reflectance spectra. Results of trial showed that the lack of pigmentation in leaves was a major factor limiting grape ripening. Significant linear regressions between Cab and total soluble solids concentration and colour density of the must were detected. Estimation of Cab using the image-calculated TCARI/OSAVI through the PROSPECT-rowMCRM model simulation for all study zones, including the specific ligninsulphonate experiment, demonstrated the potential of hyperspectral imagery for mapping Cab in vineyards for chlorosis detection using remote sensing methods. Given the described relationship between Cab and quality parameters in vineyards affected by iron chlorosis, high-spatial resolution imagery with narrow bands might enable the segmentation in areas of potential quality in the framework of precision viticulture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".