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Review
. 2019 Feb:55:1-8.
doi: 10.1016/j.copbio.2018.06.002. Epub 2018 Jul 19.

Uncovering the hidden half of plants using new advances in root phenotyping

Jonathan A Atkinson  1 , Michael P Pound  2 , Malcolm J Bennett  3 , Darren M Wells  4
Affiliations
Review

Uncovering the hidden half of plants using new advances in root phenotyping

Jonathan A Atkinson et al. Curr Opin Biotechnol. 2019 Feb.

Abstract

Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting 'big-data' is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.

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Figures

None
Graphical abstract
Figure 1
Figure 1
2D imaging of plant roots. (a) GLO-Roots [5••]. Arabidopsis plant expressing a luminescent reporter imaged on each side of the rhizotron (coloured green and magenta respectively) at 21 days after sowing (DAS). (b) GROWSCREEN-Rhizo [12]. A high-throughput automated root phenotyping platform using soil-filled rhizotrons. (c) Pouch system [9] for cereal seedlings (left panel). RootNav [51] analysis software (right panel). (d) Phytomorph [13] A high-throughput robotic imaging platform for Arabidopsis growing on agar plates.
Figure 2
Figure 2
3D tomographic imaging of plant roots. (a) X-ray CT micrograph of a wheat seedling 12 DAS. (b) MRI imaging of a maize root system at 6, 9, 12, and 15 DAS [23••]. Upper panel, MRI data (2D maximum intensity projection). Lower panel, 3D surface render. Scale bar: 20 mm. (c) Maize roots imaged using MRI-PET [24]. Two plants are growing in the same pot. The greyscale image is MRI, the colour is 11C PET data following application to a leaf of one plant. (d) OpenSimRoot [65] simulation using output from (a) to model rhizosphere N depletion. (e) Maize root imaged at 9 DAS using optical projection tomography (OPT) and PET [4]. The black and white image is OPT, the colour is 11C PET data.
Figure 3
Figure 3
Automated root image analysis software. (a) DIRT [40] measures traits based on the ‘shovelomics’ approach [37]. Root systems are washed, and imaged from above in front of a dark background. Root systems are separated from background via thresholding, and RSA traits derived from each segmented object. (b) Root-soil segmentation in X-Ray CT [61]. Root and soil pixels are identified via a Support Vector Machine classifier trained on deep-learned features. Images show the ground-truth, original image, and SVM classifier output. (c) End-to-end deep learning for root tip identification [59•]. A deep network trained on thousands of instances of root tips and negative samples can be passed over an entire image to obtain likely root tip locations.
See this image and copyright information in PMC

References

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