Reflectance spectroscopy predicts leaf functional traits across wine grape cultivars
Bibliographic record
Abstract
Societal Impact Statement Characterizing variability in crop traits is key for understanding agroecosystem responses to environmental change. However, trait data are often time‐consuming to collect and therefore still limit our understanding and predictions of agriculture responses to environmental change. We tested the ability of reflectance spectroscopy—a high‐throughput technique—to rapidly amass trait data for multiple wine grape cultivars. Reflectance spectroscopy predicts important wine grape leaf traits including photosynthesis and biochemistry with a good degree accuracy, but in a fraction of the time compared to traditional techniques. Reflectance spectroscopy can therefore rapidly characterize wine grape phenotypes and, in doing so, inform predictions of how vines, clones and cultivars will respond to environmental change. Summary Reflectance spectroscopy has emerged as a powerful tool for non‐destructive and high‐throughput phenotyping in plants. While the ability of reflectance spectroscopy to predict traits across diverse plant species and ecosystems has received considerable attention, whether or not this technique is able to quantify within species trait variation—especially physiological traits—has been less extensively explored. Quantifying intraspecific variation in traits through reflectance spectroscopy is especially appealing in agroecology, where it may present an approach for better understanding crop performance, fitness and trait‐based responses to environmental conditions. We tested if reflectance spectroscopy coupled with partial least square regression (PLSR) predicts photosynthetic carbon assimilation ( A 420 ), RuBisCO carboxylation ( V cmax ) and electron transport ( J max ) rates, as well as leaf mass per area (LMA) and leaf nitrogen (N) concentrations, across six wine grape ( Vitis vinifera ) cultivars (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot noir, Viognier, Sauvignon blanc). PLSR models showed good capability in predicting intraspecific trait variation in wine grapes, explaining up to 55%, 58%, 62% and 62% of the variation in observed J max , V cmax , leaf N and LMA values, respectively. However, predictions of A 420 were less strong, with reflectance spectra explaining only up to 29% of the variation in this trait. Our results indicate that trait variation within species and crops is less well‐predicted by reflectance spectroscopy, than trait variation that exists among species. However, our results indicate that reflectance spectroscopy still presents a viable technique for quantifying trait variation in wine grapes specifically, and agroecosystems more broadly.
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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 | 0.001 |
| 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".