Genetic analysis of predicted vegetative biomass and biomass‐related traits from digital phenotyping of strawberry

Abstract

High-throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time. DP platforms using high-resolution images captured by drones and tractor-based platforms have recently allowed the University of Florida strawberry (Fragaria × ananassa) breeding program to assess vegetative biomass at scale. Biomass has not previously been explored in a strawberry breeding context due to the labor required and the need to destroy the plant. This study aims to understand the genetic basis of predicted vegetative biomass and biomass-related traits and to chart a path for the combined use of DP and genomics in strawberry breeding. Aboveground dry vegetative biomass was estimated by adapting a previously published model using ground-truth data on a subset of breeding germplasm. High-resolution images were collected on clonally replicated trials at different time points during the fruiting season. There was moderate to high heritability (h 2 = 0.26-0.56) for predicted vegetative biomass, and genetic correlations between vegetative biomass and marketable yield were mostly positive (r G = -0.13-0.47). Fruit yield traits scaled on a vegetative biomass basis also had moderate to high heritability (h 2 = 0.25-0.64). This suggests that vegetative biomass can be decreased or increased through selection, and that marketable fruit yield can be improved without simultaneously increasing plant size. No consistent marker-trait associations were discovered via genome-wide association studies. On the other hand, predictive abilities from genomic selection ranged from 0.15 to 0.46 across traits and years, suggesting that genomic prediction will be an effective breeding tool for vegetative biomass in strawberry.

Plant phenotyping relevance

デジタル画像からイチゴの地上部乾物バイオマスを推定する手法を適応し、育種集団で大規模に適用しており、表現型取得・推定ワークフローが研究の主要部分である。

Code and data availability

The article describes digital phenotyping of strawberry biomass and genomic analyses, but no authors' public dataset, image, code, or model repository is provided. Phenotypic data are said to be in Table S1 without a public URL; software mentioned (ASRtriala, BMTME, R) are generic third-party tools, and other URLs arec

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