Alexander Bucksch

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Significance Improving the efficiency of root systems should result in crop varieties with better yields, requiring fewer chemical inputs, and that can grow in harsher environments. Little is known about the genetic factors that condition root growth because of roots’ complex shapes, the opacity of soil, and environmental influences. We designed a 3D root imaging and analysis platform and used it to identify regions of the rice genome that control several different aspects of root system growth. The results of this study should inform future efforts to enhance root architecture for agricultural benefit. Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24–37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

GiA Roots: software for the high throughput analysis of plant root system architecture

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

Image-Based High-Throughput Field Phenotyping of Crop Roots

Automatic methods developed or reproducible field-based phenotyping allow distinction of genotypes, including 13 newly accessible plant root traits. Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.

Automated Detection of Branch Dimensions in Woody Skeletons of Fruit Tree Canopies

Modeling the 3D canopy structure of trees provides the structural mapping capability on which to assign distributed values of light-driven physiological processes in tree canopies. We evaluate the potential of automatically extracted skeletons from terrestrial lidar data as a basis for modeling canopy structure. The automatic and species independent evaluation method for lidar data of trees is based on the SKELTRE algorithm. The SKELTRE skeleton is a graphical representation of the branch hierarchy. The extraction of the branch hierarchy utilizes a graph splitting procedure to extract the branches from the skeleton. Analyzing the distance between the point cloud points and the skeleton is the key to the branch diameter. Frequency distributions of branch length and diameter were chosen to test the algorithm performance in comparison to manually measured data and resulted in a correlation of up to 0.78 for the branch length and up to 0.99 for the branch diameter.

Three-Dimensional Laser Imaging as a Valuable Tool for Specifying Changes in Breast Shape After Augmentation Mammaplasty

BackgroundThree-dimensional (3D) terrestrial laser scanning (TLS) is a valuable method for measuring shapes of objects and for obtaining quantitative measurements. These qualities of the 3D laser scanner have proved to be useful in reconstructive breast surgery. This study investigated various 3D parameters to obtain an optimal objective visualization of the breast after cosmetic augmentation mammaplasty.MethodsThe objects are represented in a point cloud, which comprises millions of x, y, and z coordinates representing a virtual image. The quantification of 3D points shows changes in height (z coordinate) at any chosen point on the augmented breast (x and y coordinates). To give visual feedback on the change in dimensions, a color elevation scheme was applied on the reconstructed surface of the breast. As a quantifying description, a sagittal B-spline was chosen in a plane through the nipple to obtain the breast shape via the lateral profile.ResultsPre- and postoperative clear images were obtained. The color elevation model showed an increased projection and upper pole fullness after augmentation. The B-spline showed the gain in projection in a sagittal plane through the nipple.ConclusionsThree-dimensional TLS is capable of objectifying changes in shape after augmentation mammaplasty. This imaging technique represents superior visualization of the breast shape and can serve as a valuable tool to determine the changing dimensions of the breasts after augmentation mammaplasty.

SkelTre - fast skeletonisation for imperfect point cloud data of botanic trees

Terrestrial laser scanners capture 3D geometry as a point cloud. This paper reports on a new algorithm aiming at the skeletonisation of a laser scanner point cloud, representing a botanical tree without leafs. The resulting skeleton can subsequently be applied to obtain tree parameters like length and diameter of branches for botanic applications. Scanner-produced point cloud data are not only subject to noise, but also to undersampling and varying point densities, making it challenging to extract a topologically correct skeleton. The skeletonisation algorithm proposed in this paper consists of three steps: (i) extraction of a graph from an octree organization, (ii) reduction of the graph to the skeleton and (iii) embedding of the skeleton into the point cloud. The results are validated on laser scanner point clouds representing botanic trees. On a reference tree, the mean and maximal distance of the point cloud points to the skeleton could be reduced from 1.8 to 1.5 cm for the mean and from 15.6 to 10.5 cm for the maximum, compared to results from a previously developed method.

Localized Registration of Point Clouds of Botanic Trees

A global registration is often insufficient for estimating dendrometric characteristics of trees because individual branches of the same tree may exhibit different positions between two scanning procedures. Therefore, we introduce a localized approach to register point clouds of botanic trees. Given two roughly registered point clouds PC1 and PC2 of a tree, we apply a skeletonization method to both point clouds. Based on these two skeletons, initial correspondences between branch segments of both point clouds are established to estimate local transformation parameters. The transformation estimation relies on minimizing the distance between the points in PC1 and the skeleton of PC2. The performance of the method is demonstrated on two example trees. It is shown that significant improvements can be achieved for the registration of fine branches. These improvements are quantified as the residual point-to-line distances before and after the localized fine registration. In our experiment, the residual error after the local registration is on an average of 5 mm over 90 skeleton segments, which is about three times smaller than the average residual error of the initial rough registration.

Breast Height Diameter Estimation From High-Density Airborne LiDAR Data

High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/ m2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded.

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.

Characterizing tree growth anomaly induced by landslides using LiDAR

Disrupted vegetation is often used as an indicator for landslide activity in forested regions. The extraction of irregular trees from airborne laser scanning data remains problematic because of low quality of observed data and paucity of field data validation. We obtained high density airborne LiDAR (HDAL) data with 180 points m−2 for characterizing tree growth anomalies caused by landslides in the Barcelonnette region, the Southern French Alps. HDAL allowed the mapping of a complex landslide and its three kinematic zones. The TreeVaW method detecting trees from the HDAL data and determined their position and height, while the SkelTre-skeletonization method extracted the tree inclination. The tree growth anomalies are parameterized by tree height dissimilarities and tree inclinations. These parameters were successfully extracted from the HDAL and compared with field data. We revealed that the distribution of LiDAR-derived tree growth anomalies was statistically different for landslide areas as compared to stable areas.