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We demonstrate the suitability of SRXCT for observing the root system of wheat plants growing in two soils (Calcisol and Ultisol) in response to placement of different phosphorus fertilisers. To optimize scanning quality, we tested the use of an inverse \u2018mask\u2019 in front of the soil cores to achieve a more uniform attenuation along the sample, thereby avoiding saturation of the detector along the thinnest parts of the soil cores. Secondly, we developed a deep learning approach for segmentation and quantification of root length and diameter. Our results demonstrate the use of SRXCT as a tool for studying root system architecture in soil at high spatial resolution. 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