Comprehensive 3D phenotyping reveals continuous morphological variation across genetically diverse sorghum inflorescences

Abstract

Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity.Here, we apply X-ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three-dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor, which vary extensively in numbers, lengths, and angles of primary branches, as well as the three-dimensional shape, size, and distribution of the seed.We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative.However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

“…Compared to 2D imaging methods ( Tanabata et al, 2012 ; Evgenii et al, 2017 ; Baek et al, 2020 ), our methods quantify 3D morphological traits, such as the surface area, volume, and sphericity. Compared to the other 3D image analysis pipelines designed for grain phenotyping ( Glidewell, 2006 ; Hughes et al, 2017 ; Xiong et al, 2019 ; Hu et al, 2020 ; Li et al, 2020 ), our methods provide an additional function of non-destructively measuring morphological phenotypes of seed and fruit internal compartments. The GUI design software, 3DPheno-Seed&Fruit, is quite user-friendly, which is easy to navigate and has the excellent visualization functions for displaying phenotyping results.…”

“…But it has since been applied to a broad range of fields, like material, earth, natural, and animal sciences ( Cnudde et al, 2006 ; Schambach et al, 2010 ). Recent improvements in scanning quality, resolution, and speed allowed it to be adopted to visualize and quantify complex plant traits ( Dhondt et al, 2010 ; Hughes et al, 2017 ; Xiong et al, 2019 ; Li et al, 2020 ; Yang et al, 2020 ). For example, Arendse et al (2016) used the X-ray CT to non-destructively quantify the external and internal morphological features (volumes of aril, peel, kernel, juice content, and air space) of pomegranate fruit.…”

Deep Segmentation of Point Clouds of Wheat

“…A number of different technologies have been developed, including LASER, Time of Flight, and LIDAR to capture information from living plants for modeling (Paulus, 2019 ). Medical imaging approaches, such as μCT scanning, have also been applied to plants, particularly for ears of wheat (Hughes et al, 2019 ) and analogous structures from other crops such as sorghum inflorescences (Li et al, 2020 ) but the trade-offs involved in image acquisition generally mean that the approach is applicable to either low numbers of complete plants or somewhat larger numbers of parts of plants. The capital investment in the scanning equipment is also substantial, putting this out of reach of most researchers.…”

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

“…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.…”