Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements

Abstract

Background and aims Non- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding. Electrical methods have come into focus due to their unique sensitivity to various structural and functional root characteristics. The aim of this study is to highlight imaging capabilities of these methods with regard to crop root systems and to investigate changes in electrical signals caused by physiological reactions. Methods Spectral electrical impedance tomography (sEIT) and electrical impedance spectroscopy (EIS) were used in three laboratory experiments to characterize oilseed root systems embedded in nutrient solution. Two experiments imaged the root extension with sEIT, including one experiment monitoring a nutrient stress situation. In the third experiment electrical signatures were observed over the diurnal cycle using EIS. Results Root system extension was imaged using sEIT under static conditions. During continuous nutrient deprivation, electrical polarization signals decreased steadily. Systematic changes were observed over the diurnal cycle, indicating further sensitivity to associated physiological processes. Spectral parameters suggest polarization processes at the μm scale. Conclusions Electrical imaging methods are able to non-invasively characterize crop root systems in controlled laboratory conditions, thereby offering links to root structure and function. The methods have the potential to be upscaled to the field scale.

Plant phenotyping relevance

電気インピーダンス画像化・分光法を用いて作物根系の構造と生理状態を非侵襲的に測定する方法が研究の中心であり、根系伸長や栄養ストレス・日周生理変化の表現型取得を実証している。

Code and data availability

The paper's Data Availability statement explicitly deposits the sEIT/EIS measurement data and analysis scripts in a public Zenodo repository, which directly reproduces this paper's root-phenotyping measurements and computational analysis.