An affordable and non-invasive validated machine-aided phenotyping pipeline identifies phenotypic variation of stress resilience in alkaline calcareous soil across the life cycle in Arabidopsis thaliana

Abstract

Alkaline calcareous soils (ACS) are prevalent globally and challenge plant growth by limiting nutrient uptake, such as iron. The model plant Arabidopsis thaliana thrives in disturbed urban environments wherein ACS conditions frequently occur. Existing research largely focused on vegetatively grown A. thaliana , while there is a notable lack of studies examining phenotypic variations across the life cycle in ACS. A valuable tool for understanding plant stress resilience is machine-aided phenotyping as it is non-invasive, rapid and accurate. But it is often unavailable to individual plant labs. Here, we established and validated an affordable MicroScan with PlantEye-based machine-aided phenotyping approach, collected and correlated quantitative growth data across plant life cycles in response to ACS. We used A. thaliana wild type and the chlorotic coumarin-deficient mutant f6’h1-1 to assess weekly morphological and leaf color data both manually and using a multispectral PlantEye device. Through correlation analysis, we selected machine parameters to differentiate size and leaf chlorosis phenotypes. The correlation analysis indicated a close connection between rosette size and multiple spectral parameters, highlighting the importance of the rosette size for plant growth. Most reliable phenotyping was at the beginning bolting stage. This methodology further is validated to detect novel leaf chlorosis phenotypes of known iron deficiency mutants across growth stages. This affordable machine-aided phenotyping procedure is suitable for high-throughput accurate screening of small-grown rosette plants, such as A. thaliana , and enables the discovery of novel genetic and phenotypic variation during the life cycle for understanding plant resilience in challenging soil environments. Short summary sentence A PlantEye machine-aided non-invasive accurate and reliable phenotyping pipeline depicted the importance of the rosette size for phenotyping and detected leaf chlorosis phenotypes of A. thaliana mutants across the life-cycle on alkaline calcareous soil. Highlights and major findings: - A MicroScan PlantEye machine-aided non-invasive phenotyping pipeline was established for assessing growth data of A. thaliana across the life cycle on alkaline calcareous soil and distinguishing leaf chlorosis phenotypes. - Rosette size was found an important trait that characterizes A. thaliana growth. - Machine phenotyping was most reliable at the beginning bolting stage. - New phenotypes were detected for Fe homeostasis mutants.

Plant phenotyping relevance

植物の成長・葉色を取得するMicroScan/PlantEye機械支援フェノタイピングパイプラインの確立と検証が研究の中心であるため、収録対象です。

Code and data availability

The supplied blocks describe a PlantEye/MicroScan phenotyping pipeline with supplemental data tables, but no public repository, dataset deposit, code availability statement, or authors' public URL for the phenotyping data or analysis is provided. Analysis was done in R and SPSS without any deposit language.

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