Improved non-invasive root detection in soil using low noise magnetic resonance images

Abstract

Using magnetic resonance imaging (MRI), our established root phenotyping platform (van Dusschoten et al., 2016) can visualize and analyze plant roots in natural soil nondestructively (Pflugfelder et al., 2017). Using plant pots with 9 cm diameter and 30cm height, a root system can be scanned within 1h while roots down to diameters of 300µm can be detected and analyzed using our in-house root extraction software NMRooting (van Dusschoten et al., 2016). Thanks to automation with a pick-and-place robot the platform routinely achieves a throughput of 24 plants per day. All these values, however, are based on compromises between imaging speed and quality. In our system, the root detection limit is determined by the signal to noise ratio (SNR) of our images. The SNR can be increased by using smaller plant pots or by increasing the imaging time. In this contribution we investigate the potential gain in the root detection limit when sacrificing plant throughput in favor of image quality. We acquired low noise root images using repeated signal averaging during the measurement process. Using this approach, the root detection limit could be lowered, visualizing roots not detected by the standard imaging protocol.

Code and data availability

The supplied blocks contain only the abstract and references of this MRI root-phenotyping preprint. No public phenotype/trait datasets, MRI images, analysis code, or trained models are mentioned, and no data or code availability statement with an authors' public URL appears. NMRooting is described only as 'our in-house

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