Foliar and berry hyperspectral reflectance predicts winegrape berry composition across developmental stages and varieties
Bibliographic record
Abstract
Abstract Grapevine berry chemistry varies across varieties and developmental stages and is sensitive to environmental conditions. Efforts have sought to leverage proximal remote sensing data to exploit potential covariation of foliar traits and berry composition to enable nondestructive and scalable methods for assessing berry chemistry. Traditionally, simple vegetation indices have been used based on the assumption of a relationship between foliar coverage and berry yield and composition. However, indices assessing foliar coverage, chlorophyll content, and biomass may not be sensitive enough to track subtle variations of berry chemistry across winegrape varieties and developmental stages. Thus, in this study, we seek to leverage full‐range hyperspectral data (400–2500 nm) and a partial least squares regression (PLSR) model for assessing traditional metrics of berry composition. The benefit of PLSR models for analysis of hyperspectral data is that they can integrate subtle physiological and structural changes in plant reflectance to optimize a model. In this study, we measured leaf and berry spectra and berry composition (Brix, tartaric acid, and pH) across 23 winegrape varieties and developmental stages across 2 years from June to September in a common garden in Davis, California. Our results show that both foliar and berry hyperspectral PLSR models can be used to predict berry composition across phenological stages. This suggests that proximal remote sensing of foliage has the potential to enable rapid and nondestructive monitoring of berry developmental stages to aid in management decision‐making and harvest timing.
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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) | 0.001 | 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".