Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes. The RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. The RhizoVision Analyzer was physically validated using copper wire, and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating that differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.

17Root crown phenotyping measures the top portion of crop root systems and can be used for marker-18 assisted breeding, genetic mapping, and understanding how roots influence soil resource 19 acquisition. Several imaging protocols and image analysis programs exist, but they are not 20 optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision 21 Crown platform integrates an imaging unit, image capture software, and image analysis software 22 that are optimized for reliable extraction of measurements from large numbers of root crowns. The 23 hardware platform utilizes a back light and a monochrome machine vision camera to capture root 24 crown silhouettes. RhizoVision Imager and RhizoVision Analyzer are free, open-source software 25that streamline image capture and image analysis with intuitive graphical user interfaces. 26 RhizoVision Analyzer was physically validated using copper wire and features were extensively 27 validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This 28 platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean 29 (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 30 lines were phenotyped. Principal component analysis indicated similar correlations among features 31 in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating 32 differences in species and populations need to be considered. The integrated RhizoVision Crown 33 platform facilitates high-throughput phenotyping of crop root crowns, and sets a standard by which 34 open plant phenotyping platforms can be benchmarked.35 36Roots serve as the interface between the plant and the complex soil environment with key 37 functions of water and nutrient extraction from soils (Lynch, 1995;Meister et al., 2014). Root 38 system architecture (RSA) refers to the shape and spatial arrangement of root systems within the 39 soil, which plays an important role in plant fitness, crop performance, and agricultural productivity 40 (Lynch, 1995;York et al., 2013;Rogers and Benfey, 2015). RSA is shaped by the interactions 41 between genetic and environmental components, and it influences the total volume of soil that 42 roots can explore (Rogers and Benfey, 2015). Many root phenes (or elemental units of phenotype 43 Lynch, 2011; Pieruschka and Poorter, 2012; York et al., 2013) shape the final RSA, including the 44 number, length, growth angle, elongation rate, diameter, and branching of axial and lateral roots 45 (Bishopp and Lynch, 2015). Understanding the contribution of RSA phenes to crop performance 46 is of key importance in food security and for breeding of more productive and resilient varieties in 47 a changing environment. 48 Because roots are hidden underground and require considerable effort to characterize, research 49 on roots lags behind that on aboveground parts of the plant (Eshel and Beeckman, 2013), and the 50 genetic and functional basis of RSA remains ob...

A b s t r a c t 15 Background: Root crown phenotyping has linked root properties to shoot mass, nutrient uptake, and 16 yield in the field, which increases the understanding of soil resource acquisition and presents 17 opportunities for breeding. The original methods using manual measurements have been largely 18 supplanted by image-based approaches. However, most image-based systems have been limited to one 19 or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root 20 crown phenotyping system is introduced that takes images from five perspectives simultaneously, 21 constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the 22 Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to 23 distinguish plant root pixels from background pixels in color images and using hardware acceleration 24 (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for 25 image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine 26 max (L.) Merr.] cultivars released between 1930 and 2005 . 27 Results: Relative to previous reports of imaging throughput, this system provides greater throughput 28 with sustained rates of 1.66 root crowns min -1 . The EM-GMM segmentation algorithm with hardware 29 acceleration was able to segment images in 10 s, faster than previous methods, and the output images 30 were consistently better connected with less loss of fine detail. Image-based phenes had similar 31 heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine 32 Radius Frequency. Correlations were also noted, especially among the manual Complexity score and 33 phenes such as number of roots and Total Root Length. Averaging phenes across perspectives 34 3 generally increased heritability, and no single perspective consistently performed better than others. 35Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were 36 the most sensitive to perspective with decreased correlations among perspectives. 37Conclusion: The substantial heritabilities measured for many phenes suggest that they are potentially 38 useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no 39 single perspective consistently performed better than others. Thus, as illustrated here for soybean, 40 multiple perspectives may be beneficial for root crown phenotyping systems. This system can 41 contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security 42 and sustainability. 43

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