Image-based phenotyping of faba bean genetic resources for water deficit responses under controlled conditions

Abstract

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-adapted germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.

Plant phenotyping relevance

多センサー表現型プラットフォームを用いた画像ベースのデジタル biomass 推定を検証し、植物形質評価への利用可能性を示しており、表現型取得法が中心的です。

Code and data availability

The paper's imaging and numerical phenotyping data (RGB/digital biomass, chlorophyll fluorescence, WUE) are not yet publicly available; the authors state they will be deposited in PHIS and are available upon request. No public analysis code or trained models are disclosed.

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