Hyperspectral Sensing for High-Throughput Screening of Boron Tolerance in Grapevines

Abstract

Abstract Boron (B) is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. In this study, we evaluated the potential of hyperspectral radiometry combined with machine learning to identify B-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under B treatments ranging from 0.5 to 8 ppm, measuring leaf B accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance (R 380 –R 1100 nm). Our results revealed substantial genotypic variation in B exclusion, with some genotypes maintaining low leaf B content despite high external concentrations. Classification models (Partial Least Squares Discriminant Analysis and Random Forest classification) outperformed regression models (Partial Least Squares Regression and Random Forest regression) in distinguishing B-excluding genotypes, achieving moderate to high accuracy within just eight days after stress initiation. Vegetation indices such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), Structure Insensitive Pigment Index (SIPI), and Chlorophyll Index (CI) indicated that B stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf B content proved more challenging, simulations showed that even modest prediction accuracies can substantially boost genetic gains if larger populations are screened, and selection intensities are increased. These findings underscore the value of hyperspectral radiometry for high-throughput phenotyping, allowing breeders to rapidly identify and advance B-tolerant rootstocks.

Plant phenotyping relevance

ブドウ台木のホウ素耐性を対象に、ハイパースペクトル計測と機械学習による表現型スクリーニング手法を開発・評価しており、表現型取得と分類が研究の中心である。

Code and data availability

The paper reports hyperspectral phenotyping of grapevine boron tolerance with machine learning, but the only availability statement points to supplementary files accompanying the manuscript, with no public repository, dataset URL, or author code deposit. No paper-specific public asset is actionable.

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