Rapid identification of boron-tolerant grapevine rootstocks via leaf spectroscopy

Abstract

Boron is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. This study evaluated the potential of leaf spectroscopy combined with machine learning to identify boron-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under boron treatments ranging from 0.5 to 8 ppm, measuring leaf boron accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance. The results revealed substantial genotypic variation in boron exclusion, with some genotypes maintaining low leaf boron concentration despite high substrate 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 boron-excluding genotypes, achieving 68 % to 79 % accuracy within just eight days after stress initiation. Reflectance-based vegetation indices such as the Normalized Difference Vegetation Index, Photochemical Reflectance Index, Structure Insensitive Pigment Index, and Chlorophyll Index indicated that boron stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf boron concentration proved more challenging, simulations showed that even modest prediction accuracies (~60 %) can substantially boost genetic gains if larger populations are screened and selection intensities are increased. These findings underscore the value of leaf spectroscopy for high-throughput phenotyping, allowing breeders to rapidly identify and advance boron-tolerant rootstocks.

Plant phenotyping relevance

葉分光と機械学習を用いた耐性根株の迅速な表現型推定・選抜が中心であり、反射スペクトルからホウ素耐性や関連生理形質を高スループットに評価する方法を実質的に適用・検証している。

Code and data availability

The supplied blocks describe the greenhouse experiment, leaf reflectance measurements, ICP-AES boron analysis, and PLS/RF modelling, but contain no data availability statement, public dataset deposit, author code repository, or supplement with the paper's spectral/phenotype data. The only code mentioned is the third R/

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