Leaf hyperspectral reflectance detects pre‐visual stress to <i>Fusarium</i> wilt in strawberries

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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Publication Details

Journal
The Plant Phenome Journal
Published
2025-10-31
DOI
https://doi.org/10.1002/ppj2.70046
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Leaf hyperspectral reflectance detects pre‐visual stress to Fusarium wilt in strawberries

Christopher Y. S. Wong, Rishav Ray, Troy S. Magney, Dominique D. A. Pincot et al.
The Plant Phenome Journal
Remote Sensing in Agriculture
article

Leaf hyperspectral reflectance detects pre‐visual stress to Fusarium wilt in strawberries

Christopher Y. S. Wong, Rishav Ray, Troy S. Magney, Dominique D. A. Pincot, J. Mason Earles, Jessie L.‐S. Au, Frank N. Martin
article en

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.

The Plant Phenome JournalVol. 8(1)
University of New Brunswick (CA), United States Department of Agriculture (US), Plant (United States) (US), University of Montana (US), University of California, Davis (US)
National Institute of Food and Agriculture
Openalex Percentile: Top 69%
Remote Sensing in Agriculture
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