Non-destructive Spatial Reconstruction of Plant Leaf Starch Using Reduced-Band SWIR Spectroscopy and Chemometric Modeling

Abstract

1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.

Plant phenotyping relevance

植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。

Code and data availability

The supplied blocks describe a SWIR hyperspectral imaging workflow with PLSR modeling for leaf starch prediction, but contain no data availability statement, code deposit, repository link, or supplement reference for the hyperspectral dataset, images, or analysis code. Only the article DOI appears; no authors' public,

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