Closing the phenotyping gap with non-invasive belowground field phenotyping

Abstract

Abstract. Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field conditions in real soils. Whereas a number of techniques exist for aboveground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first data set of the new HYDRAS (HYdrology, Drones and RAinout Shelters) open-access field-phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technological readiness level closer to what breeders and researchers need. This paper investigates whether electrical resistivity tomography (ERT) provides sufficient precision and accuracy to distinguish between belowground plant traits of different genotypes of the same crop species. The proof-of-concept experiment was conducted in 2023, with three distinct soybean genotypes known for their contrasting reactions to drought stress. We illustrate how this new infrastructure addresses the issues of depth resolution, automated data processing, and phenotyping indicator extraction. The work shows that electrical resistivity tomography is ready to complement drone-based field-phenotyping techniques to accomplish whole-plant high-throughput field phenotyping.

Plant phenotyping relevance

ERTとドローン画像を用いた非侵襲的な地下部形質の取得・抽出精度を検証する新規圃場フェノタイピング基盤の研究であり、方法と技術性能が中心です。

Code and data availability

The paper describes a POC2023 ERT/drone phenotyping dataset and an automated processing pipeline, and references an 'accompanying Jupyter notebook' and a 'GitLab repository' for the HTML report, but no explicit public URL or deposit identifier for these authors' assets is given in the supplied blocks. The allowed URLs,

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