RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping

Abstract

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...

Exploiting Natural Variation in Crown Root Traits via Genome-Wide Association Studies in Maize

“…The challenges associated with evaluating root traits in the eld have hindered the characterisation of the genetic mechanisms regulating root traits. Several new methods have recently been developed for relatively high-throughput and accurate analyses of roots under eld conditions [33]. In this study, shovelomics-based experiments were conducted to evaluate the CRA, CRD, and CRN of 348 inbred lines at maturity in three eld trials.…”

Functional phenomics and genetics of the root economics space in winter wheat using high-throughput phenotyping of respiration and architecture

“…After the root respiration measurements, roots from each plant were spread in a 5 mm layer of water in transparent acrylic trays and imaged with a flatbed scanner equipped with a transparency unit (Epson Expression 12000XL, Epson America) at a resolution of 600 dpi. Images were analyzed using RhizoVision Explorer version 2.0.2 (Seethepalli & York, 2020) with algorithms described by Seethepalli et al (2020) with the options for image thresholding level, filter noisy components, threshold for root pruning being set at 205 pixel intensity, 0.2 mm 2 , and 1 pixel, respectively. A root diameter threshold of 0.30 mm was used to distinguish axial roots from lateral roots (Figure 1e).…”

Complementary Phenotyping of Maize Root Architecture by Root Pulling Force and X-Ray Computed Tomography

“…RPF is generally correlated with greater root biomass and branching, but more nuanced interpretations and its relationship with recently tractable architectural measurements have yet to be established. More intricate phenotyping of root system architecture can be performed upon two-dimensional images of either field-excavated root crowns, or young gel-media grown root systems, followed by analysis with specialized software (Le Bot et al, 2010;Lobet et al, 2011;Galkovskyi et al, 2012;Colombi et al, 2015;Das et al, 2015;Symonova et al, 2015;Delory et al, 2018;Seethepalli et al, 2020). Such methods have been used to quantify root system architecture in diverse crops such as maize, wheat, rice, and cowpea (Bucksch et al, 2014;Canè et al, 2014;Burridge et al, 2017;Wedger et al, 2019).…”

“…By scaling this technique to mapping populations, studies have identified new univariate or multivariate root QTLs, demonstrating the value of high-throughput and high-information-content trait capture for dissection of plant architecture (Topp et al, 2013;Zurek et al, 2015). Other 3D-based solutions include the use of X-ray computed tomography (XRT), which is capable of imaging any plant structure, including roots within soil based upon physical density properties (Mairhofer et al, 2012;Mooney et al, 2012;Bao et al, 2014;Rogers et al, 2016;Duncan et al, 2019;Li et al, 2019;Li et al, 2020;Helliwell et al). While XRT has been applied to plant physiology in some form for nearly two decades, instrument accessibility and technical limitations typically restrict its use to small plant structures, low throughput, and/or limited fields of view.…”