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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
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
Ahstract-Modeling virtual plant roots remains a hot topic in both industry world and academic community. In this paper, we propose a new alhagi pseudalhagi root growth model based on L-system, an effective approach to virtual plant modeling. As the root system of alhagi pseudalhagi proves most typical and valuable for research, we give brief review and analysis of the morphological feature. Modeling process and results using L-system are also described in detail by virtue of computer graphic technique. Finally, we discuss the application and future work of L-system in the domain of virtual plant modeling.Keywords-L-system; virtual plant; root growth model; alhagi pseudalhagi root system.I.
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