Leaf hyperspectral reflectance detects pre‐visual stress to <i>Fusarium</i> wilt in strawberries
Bibliographic record
Abstract
Abstract Fusarium wilt, caused by Fusarium oxysporum , threatens global food security and high‐value crops like strawberries ( Fragaria × ananassa ) in California. Traditional detection, reliant on visual symptoms, often comes too late for intervention. This study uses leaf‐level hyperspectral reflectance to detect physiological changes in resistant and susceptible strawberry cultivars. Weekly measurements of leaf reflectance, stomatal conductance, and chlorophyll fluorescence were collected across 14 cultivars inoculated with the pathogen. We examined both common spectral vegetation indices (SVIs) and patterns across the full hyperspectral range. In addition to SVIs, we assessed the full reflectance space (400–2515 nm) using principal coordinates analysis on Bray–Curtis dissimilarity and calculated coefficient of variation to evaluate spectral sensitivity to disease progression. Susceptible plants showed spectral shifts 3–5 weeks before visible symptoms. The normalized phaeophytinization index was most sensitive, indicating early chlorophyll degradation, while normalized difference vegetation index and photochemical reflectance index captured structural and physiological changes prior to visible infection. Spectral indices outperformed physiological traits, with stronger responses in stomatal conductance and leaf temperature than fluorescence. Hyperspectral disease associations highlight predictive power concentrated in the red and near‐infrared range, highlighting the potential of multispectral tools for field applications.
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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 | 0.000 | 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".